{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d90cbc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from vk_download import main\n",
    "import scipy.sparse as sprs\n",
    "#import vk\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "#import vk\n",
    "import time\n",
    "#from vk.exceptions import VkAPIError\n",
    "from requests.exceptions import ConnectionError \n",
    "from requests.exceptions import ReadTimeout \n",
    "import pandas as pd\n",
    "import random\n",
    "import datetime\n",
    "from tqdm.auto import tqdm\n",
    "from threading import Thread\n",
    "from multiprocessing import Process\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix, lil_matrix\n",
    "import scipy.sparse as sprs\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from IPython.display import clear_output\n",
    "from statsmodels.stats.proportion import proportion_confint\n",
    "from scipy import stats\n",
    "from scipy.stats import mannwhitneyu\n",
    "import networkx as nx\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a9752a-e68a-4f73-88d1-8d3f4275b462",
   "metadata": {},
   "source": [
    "# Set thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9dfaf7bc-c887-422f-8904-10ed452878c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "OpThr = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
    "FrNumberThr = [0, 6, 12, 22]\n",
    "AgeThr = [0, 29, 35, 44, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5dd5e2d-fb77-46bb-bd60-ddf8160f2a2f",
   "metadata": {},
   "source": [
    "# Choose city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf7d8c14-c6c3-47b0-aa11-f98b0b0e2191",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = 'MainCity'  # This city was used in analysis\n",
    "\n",
    "# These two cities were used to check robustness of our results\n",
    "\n",
    "#folder = 'City(1)'  # Robustness 1\n",
    "#folder = 'City(2)'  # Robustness 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3af30b",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c1b7cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>OpinionBefore</th>\n",
       "      <th>OpinionAfter</th>\n",
       "      <th>FriendsBefore</th>\n",
       "      <th>FriendsAfter</th>\n",
       "      <th>NewFriends</th>\n",
       "      <th>DeletedFriends</th>\n",
       "      <th>FolloweesBefore</th>\n",
       "      <th>FolloweesAfter</th>\n",
       "      <th>NewFollowees</th>\n",
       "      <th>DeletedFollowees</th>\n",
       "      <th>FriendsOpinionBeforeAvg</th>\n",
       "      <th>FriendsOpinionAfterAvg</th>\n",
       "      <th>FriendsOpinionBeforeStd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  OpinionBefore  OpinionAfter  FriendsBefore  FriendsAfter  \\\n",
       "0   35    1           0.45          0.46             21            21   \n",
       "1   41    2           0.42          0.42              5             5   \n",
       "2   32    2           0.85          0.87             24            25   \n",
       "3   36    2           0.34          0.34             18            18   \n",
       "4   32    1           0.50          0.50             26            26   \n",
       "\n",
       "   NewFriends  DeletedFriends  FolloweesBefore  FolloweesAfter  NewFollowees  \\\n",
       "0           1               1               45              44             3   \n",
       "1           0               0               13              13             0   \n",
       "2           3               2               85             100            15   \n",
       "3           0               0               27              31             4   \n",
       "4           0               0               14              14             0   \n",
       "\n",
       "   DeletedFollowees  FriendsOpinionBeforeAvg  FriendsOpinionAfterAvg  \\\n",
       "0                 4                     0.42                    0.41   \n",
       "1                 0                     0.45                    0.46   \n",
       "2                 0                     0.53                    0.51   \n",
       "3                 0                     0.40                    0.39   \n",
       "4                 0                     0.52                    0.53   \n",
       "\n",
       "   FriendsOpinionBeforeStd  \n",
       "0                     0.23  \n",
       "1                     0.09  \n",
       "2                     0.25  \n",
       "3                     0.19  \n",
       "4                     0.25  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv(f'{folder}/UsersAttributesGiant.csv')\n",
    "#X = pd.read_csv(f'{folder}/users_attributes_all_giant(ext).csv')\n",
    "\n",
    "A1 = sprs.load_npz(f'{folder}/AdjMatrix(1)Giant.npz')\n",
    "A2 = sprs.load_npz(f'{folder}/AdjMatrix(2)Giant.npz')\n",
    "\n",
    "S1 = sprs.load_npz(f'{folder}/Followees(1)Giant.npz')\n",
    "S2 = sprs.load_npz(f'{folder}/Followees(2)Giant.npz')\n",
    "\n",
    "#print(data.shape, A1.shape, A2.shape)\n",
    "\n",
    "DeltaA = A2 - A1\n",
    "\n",
    "DeltaAData = DeltaA.data\n",
    "\n",
    "N = X.shape[0]  # number of users in the sample\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c2b363-fa71-40e1-be29-816736fde538",
   "metadata": {},
   "source": [
    "# Prepare masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6fa27eea-9407-418d-9c7b-838bef1b1375",
   "metadata": {},
   "outputs": [],
   "source": [
    "MaskFemale = X['sex'] == 1\n",
    "MaskMale= X['sex'] == 2\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "MaskStrongConsBefore = X['OpinionBefore'] <= OpThr[1]\n",
    "MaskModConsBefore = (X['OpinionBefore'] > OpThr[1]) & (X['OpinionBefore'] <= OpThr[2])\n",
    "MaskModBefore = (X['OpinionBefore'] > OpThr[2]) & (X['OpinionBefore'] < OpThr[3])\n",
    "MaskModLibBefore = (X['OpinionBefore'] >= OpThr[3]) & (X['OpinionBefore'] < OpThr[4])\n",
    "MaskStrongLibBefore = X['OpinionBefore'] >= OpThr[4]\n",
    "\n",
    "OpinionMasksBefore = [MaskStrongConsBefore, \n",
    "                      MaskModConsBefore, \n",
    "                      MaskModBefore, \n",
    "                      MaskModLibBefore, \n",
    "                      MaskStrongLibBefore]\n",
    "\n",
    "\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "MaskStrongConsAfter = X['OpinionAfter'] <= OpThr[1]\n",
    "MaskModConsAfter = (X['OpinionAfter'] > OpThr[1]) & (X['OpinionAfter'] <= OpThr[2])\n",
    "MaskModAfter = (X['OpinionAfter'] > OpThr[2]) & (X['OpinionAfter'] < OpThr[3])\n",
    "MaskModLibAfter = (X['OpinionAfter'] >= OpThr[3]) & (X['OpinionAfter'] < OpThr[4])\n",
    "MaskStrongLibAfter = X['OpinionAfter'] >= OpThr[4]\n",
    "\n",
    "OpinionMasksAfter = [MaskStrongConsAfter, \n",
    "                     MaskModConsAfter, \n",
    "                     MaskModAfter, \n",
    "                     MaskModLibAfter, \n",
    "                     MaskStrongLibAfter]\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "OpinionMaskNames = ['Strong conservatives', 'Conservatives', 'Moderates', 'Liberals', 'StrongLiberals']\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "MaskFew = X['FriendsBefore'] <= FrNumberThr[1]\n",
    "MaskAvg = (X['FriendsBefore'] > FrNumberThr[1]) & (X['FriendsBefore'] <= FrNumberThr[2])\n",
    "MaskMany = (X['FriendsBefore'] > FrNumberThr[2]) & (X['FriendsBefore'] <= FrNumberThr[3])\n",
    "MaskHub = X['FriendsBefore'] > FrNumberThr[3]\n",
    "\n",
    "MasksDegree = [MaskFew, MaskAvg, MaskMany, MaskHub]\n",
    "MasksDegreeNames = ['Few', 'Avg', 'Many', 'Hub']\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "MaskYoung = X['age'] <= AgeThr[1]\n",
    "MaskMiddle = (X['age'] > AgeThr[1]) & (X['age'] <= AgeThr[2])\n",
    "MaskOld = (X['age'] > AgeThr[2]) & (X['age'] <= AgeThr[3])\n",
    "MaskVeryOld = X['age'] > AgeThr[3]\n",
    "\n",
    "MasksAge = [MaskYoung, MaskMiddle, MaskOld, MaskVeryOld]\n",
    "MasksAgeNames = ['Young', 'Middle', 'UpperMiddle', 'Old']\n",
    "\n",
    "################################################################################################\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a9fab4-7c93-49ab-9915-22b383353fec",
   "metadata": {},
   "source": [
    "# Some statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "593d13a3-d3b6-42db-b059-dddf5f3a9412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3768"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(abs(X['OpinionAfter'] - X['OpinionBefore']) > 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01d9e3cd-545c-4ff1-beea-5a332140f8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1352428125336492"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(abs(X['OpinionAfter'] - X['OpinionBefore']) > 0.05) / N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4705155-5ffc-4404-8d01-572036a1d010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of pairs: 388103730.0\n",
      "\n",
      "Number of edges at time t1: 229066.0\n",
      "\n",
      "Number of disconnected pairs at time t1: 387874664.0\n",
      "\n",
      "Number of edges at time t2: 244749.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of pairs: {N * (N - 1)/2}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of edges at time t1: {A1.sum()/2}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of disconnected pairs at time t1: {N * (N - 1)/2 - A1.sum()/2}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of edges at time t2: {A2.sum()/2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884ca26e-45a2-4f6d-84d8-8c0b76a38c45",
   "metadata": {},
   "source": [
    "# First, let us present the changes in the social graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b63893fb-e096-499b-a98b-c899b9d23a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of new ties: 20245\n",
      "\n",
      "Number of deleted ties: 4562\n"
     ]
    }
   ],
   "source": [
    "deltaA = A2 - A1\n",
    "\n",
    "print(f'Number of new ties: {int(deltaA[deltaA > 0].sum() / 2)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of deleted ties: {int(deltaA[deltaA < 0].sum() * (-1) / 2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a52cb40-ab5c-4a38-a0eb-c7d96f1c2e3c",
   "metadata": {},
   "source": [
    "# Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "295490d5-6e4d-4619-9f86-7a32f49ea551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age -- number of friends: (-0.14296615132985357, 3.931699656641351e-127)\n",
      "\n",
      "age -- number of new friends: (-0.05073146766258945, 2.3919988705394622e-17)\n",
      "\n",
      "age -- number of deleted friends: (-0.16623216683253175, 8.675455291003229e-172)\n",
      "\n",
      "age -- gender: PointbiserialrResult(correlation=-0.17166950625647376, pvalue=3.0409112021381234e-183)\n",
      "\n",
      "age -- new followees: (-0.05327566469682102, 5.661206188388526e-19)\n",
      "\n",
      "age -- deleted followees: (-0.11925156238905951, 9.009997497710054e-89)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "age -- number of followees: (-0.13187453455972623, 2.6703043627520136e-108)\n",
      "\n",
      "age -- number of new followees: (-0.05327566469682102, 5.661206188388526e-19)\n",
      "\n",
      "age -- number of new followees: (-0.11925156238905951, 9.009997497710054e-89)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "age -- opinion: (-0.20058483031626, 8.86033814151957e-251)\n",
      "\n",
      "####################################################################################\n",
      "####################################################################################\n",
      "\n",
      "opinion -- number of friends: (0.050809492558662135, 2.138250291922091e-17)\n",
      "\n",
      "opinion -- number of new friends: (0.02322335744320337, 0.00010588182121431889)\n",
      "\n",
      "opinion -- number of deleted friends: (0.056040313074112114, 7.900815016103638e-21)\n",
      "\n",
      "opinion -- number of followees: (-0.3034022304516945, 0.0)\n",
      "\n",
      "opinion -- number of new followees: (-0.045931488551357834, 1.7144586353151363e-14)\n",
      "\n",
      "opinion -- number of deleted followees: (0.013727291675354244, 0.02194538800480243)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "opinion -- gender: PointbiserialrResult(correlation=-0.008161579342596483, pvalue=0.17311555032687337)\n",
      "\n",
      "####################################################################################\n",
      "####################################################################################\n",
      "\n",
      "number of friends -- number of new friends: (0.442301010948791, 0.0)\n",
      "\n",
      "number of friends -- number of deleted friends: (0.2657461977479265, 0.0)\n",
      "\n",
      "number of friends -- number of followees (0.12229995891316735, 2.650600384274062e-93)\n",
      "\n",
      "number of friends -- number of new followees (-0.01461782768777353, 0.01468838611731022)\n",
      "\n",
      "number of friends -- number of deleted followees (0.036194024457586536, 1.5120346059425067e-09)\n",
      "\n",
      "####################################################################################\n",
      "####################################################################################\n",
      "\n",
      "number of followees -- number of new followees (0.2638552470701995, 0.0)\n",
      "\n",
      "number of followees -- number of deleted followees (0.32503465159458145, 0.0)\n",
      "\n",
      "####################################################################################\n",
      "####################################################################################\n",
      "\n",
      "gender -- number of friends PointbiserialrResult(correlation=0.08727779485570132, pvalue=3.009910925550995e-48)\n",
      "\n",
      "gender -- number of new friends PointbiserialrResult(correlation=0.005626686906371371, pvalue=0.3476536374985453)\n",
      "\n",
      "gender -- number of deleted friends PointbiserialrResult(correlation=-0.017369799006113153, pvalue=0.003738912415628943)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "gender -- number of followees PointbiserialrResult(correlation=-0.12735450554161415, pvalue=4.4786057637723826e-101)\n",
      "\n",
      "gender -- number of new followees PointbiserialrResult(correlation=-0.09117070338426785, pvalue=1.6731554669774483e-52)\n",
      "\n",
      "gender -- number of deleted followees PointbiserialrResult(correlation=-0.05037692297809718, pvalue=3.973210663544877e-17)\n",
      "####################################################################################\n",
      "\n",
      "number of new friends -- number of deleted friends (0.18084318667459076, 1.8604835436361506e-203)\n",
      "\n",
      "number of new friends -- number of followees (0.0593605610972613, 3.5278682466953666e-23)\n",
      "\n",
      "number of new friends -- number of new followees (0.18048657608462024, 1.1920255312962398e-202)\n",
      "\n",
      "number of new friends -- number of deleted followees (0.06776132976272205, 1.0099401588037747e-29)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "number of deleted friends -- number of followees (0.08520469295508327, 4.6585546707716876e-46)\n",
      "\n",
      "number of deleted friends -- number of new followees (0.07241066523863059, 1.0325161636364271e-33)\n",
      "\n",
      "number of deleted friends -- number of deleted followees (0.12504567058987232, 1.7436696688355366e-97)\n",
      "\n",
      "####################################################################################\n",
      "\n",
      "number of new followees -- number of deleted followees (0.18948703511834386, 1.5722597932695314e-223)\n"
     ]
    }
   ],
   "source": [
    "print('age -- number of friends:', stats.pearsonr(X['age'], X['FriendsBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- number of new friends:', stats.pearsonr(X['age'], X['NewFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- number of deleted friends:', stats.pearsonr(X['age'], X['DeletedFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- gender:', stats.pointbiserialr(X['age'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- new followees:', stats.pearsonr(X['age'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- deleted followees:', stats.pearsonr(X['age'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- number of followees:', stats.pearsonr(X['age'], X['FolloweesBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- number of new followees:', stats.pearsonr(X['age'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- number of new followees:', stats.pearsonr(X['age'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('age -- opinion:', stats.pearsonr(X['age'], X['OpinionBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of friends:', stats.pearsonr(X['OpinionBefore'], X['FriendsBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of new friends:', stats.pearsonr(X['OpinionBefore'], X['NewFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of deleted friends:', stats.pearsonr(X['OpinionBefore'], X['DeletedFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of followees:', stats.pearsonr(X['OpinionBefore'], X['FolloweesBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of new followees:', stats.pearsonr(X['OpinionBefore'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- number of deleted followees:', stats.pearsonr(X['OpinionBefore'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('opinion -- gender:', stats.pointbiserialr(X['OpinionBefore'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of friends -- number of new friends:', stats.pearsonr(X['FriendsBefore'], X['NewFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of friends -- number of deleted friends:', stats.pearsonr(X['FriendsBefore'], X['DeletedFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of friends -- number of followees', stats.pearsonr(X['FriendsBefore'], X['FolloweesBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of friends -- number of new followees', stats.pearsonr(X['FriendsBefore'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of friends -- number of deleted followees', stats.pearsonr(X['FriendsBefore'], X['DeletedFollowees']))\n",
    "\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of followees -- number of new followees', stats.pearsonr(X['FolloweesBefore'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of followees -- number of deleted followees', stats.pearsonr(X['FolloweesBefore'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of friends', stats.pointbiserialr(X['FriendsBefore'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of new friends', stats.pointbiserialr(X['NewFriends'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of deleted friends', stats.pointbiserialr(X['DeletedFriends'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of followees', stats.pointbiserialr(X['FolloweesBefore'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of new followees', stats.pointbiserialr(X['NewFollowees'], X['sex']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('gender -- number of deleted followees', stats.pointbiserialr(X['DeletedFollowees'], X['sex']))\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of new friends -- number of deleted friends', stats.pearsonr(X['NewFriends'], X['DeletedFriends']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of new friends -- number of followees', stats.pearsonr(X['NewFriends'], X['FolloweesBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of new friends -- number of new followees', stats.pearsonr(X['NewFriends'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of new friends -- number of deleted followees', stats.pearsonr(X['NewFriends'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of deleted friends -- number of followees', stats.pearsonr(X['DeletedFriends'], X['FolloweesBefore']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of deleted friends -- number of new followees', stats.pearsonr(X['DeletedFriends'], X['NewFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of deleted friends -- number of deleted followees', stats.pearsonr(X['DeletedFriends'], X['DeletedFollowees']))\n",
    "\n",
    "print('')\n",
    "\n",
    "print('####################################################################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('number of new followees -- number of deleted followees', stats.pearsonr(X['NewFollowees'], X['DeletedFollowees']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79dbd234-7881-4d6a-a61c-851a1438c1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.regplot(X['OpinionBefore'],X['age'], color ='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa57dbf-f95d-4782-9414-a9146e22ec1b",
   "metadata": {},
   "source": [
    "# Some statistics on how many friends and followees the users have and how actively they adjsut their connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d136dc97-234f-4568-ab7b-da9bc1f1a82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27861"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "31423160",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of friends at time t1: 16.44\n",
      "\n",
      "The maximal number of friends at time t1: 566\n",
      "\n",
      "Mean number of friends at time t2: 17.57\n",
      "\n",
      "The maximal number of friends at time t2: 580\n",
      "\n",
      "The number of users with no friends at time t2: 42\n",
      "\n",
      "Number of new edges: 20245, 1.45 new friends per user\n",
      "\n",
      "Number of deleted edges: 4562, 0.33 deleted friends per user\n",
      "\n",
      "------------------------------------------------------------------------\n",
      "\n",
      "The mean number of followees at time t1: 62\n",
      "\n",
      "The mean number of followees at time t2: 64\n",
      "\n",
      "The maximal number of followees at time t1: 199\n",
      "\n",
      "The maximal number of followees at time t2: 198\n"
     ]
    }
   ],
   "source": [
    "FriendsBeforeMean = X['FriendsBefore'].mean()\n",
    "FriendsBeforeMax = X['FriendsBefore'].max()\n",
    "FriendsAfterMean = X['FriendsAfter'].mean()\n",
    "FriendsAfterMax = X['FriendsAfter'].max()\n",
    "\n",
    "print(f'Mean number of friends at time t1: {round(FriendsBeforeMean, 2)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The maximal number of friends at time t1: {FriendsBeforeMax}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Mean number of friends at time t2: {round(FriendsAfterMean, 2)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The maximal number of friends at time t2: {FriendsAfterMax}')\n",
    "\n",
    "print('')\n",
    "\n",
    "NoFr = X[X['FriendsAfter'] == 0].shape[0]\n",
    "\n",
    "print(f'The number of users with no friends at time t2: {NoFr}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of new edges: {int(len(DeltaAData[DeltaAData > 0])/2)}, {round(len(DeltaAData[DeltaAData > 0]) / N, 2)} new friends per user')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of deleted edges: {int(len(DeltaAData[DeltaAData < 0])/2)}, {round(len(DeltaAData[DeltaAData < 0]) / N, 2)} deleted friends per user')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('------------------------------------------------------------------------')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The mean number of followees at time t1: {int(np.array(S1.sum(axis=1)).ravel().mean())}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The mean number of followees at time t2: {int(np.array(S2.sum(axis=1)).ravel().mean())}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The maximal number of followees at time t1: {int(np.array(S1.sum(axis=1)).ravel().max())}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'The maximal number of followees at time t2: {int(np.array(S2.sum(axis=1)).ravel().max())}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1176827c-2d67-42ba-9e02-999210b0c865",
   "metadata": {},
   "source": [
    "# About hubs (nodes with many friends) ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8eaa4f42-4080-4ae5-bcaf-08c849f21a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>OpinionBefore</th>\n",
       "      <th>OpinionAfter</th>\n",
       "      <th>FriendsBefore</th>\n",
       "      <th>FriendsAfter</th>\n",
       "      <th>NewFriends</th>\n",
       "      <th>DeletedFriends</th>\n",
       "      <th>FolloweesBefore</th>\n",
       "      <th>FolloweesAfter</th>\n",
       "      <th>NewFollowees</th>\n",
       "      <th>DeletedFollowees</th>\n",
       "      <th>FriendsOpinionBeforeAvg</th>\n",
       "      <th>FriendsOpinionAfterAvg</th>\n",
       "      <th>FriendsOpinionBeforeStd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>12368</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
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       "      <td>566</td>\n",
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       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  OpinionBefore  OpinionAfter  FriendsBefore  FriendsAfter  \\\n",
       "12368   22    2            0.5           0.5            566           580   \n",
       "\n",
       "       NewFriends  DeletedFriends  FolloweesBefore  FolloweesAfter  \\\n",
       "12368          23               9               12              12   \n",
       "\n",
       "       NewFollowees  DeletedFollowees  FriendsOpinionBeforeAvg  \\\n",
       "12368             0                 0                     0.34   \n",
       "\n",
       "       FriendsOpinionAfterAvg  FriendsOpinionBeforeStd  \n",
       "12368                    0.34                      0.2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LargestHub = X['FriendsBefore'].max()\n",
    "\n",
    "X[ X['FriendsBefore'] == LargestHub ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "75576b7f-3d89-4bb8-a261-d6135301ffbd",
   "metadata": {},
   "outputs": [
    {
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       "      <th>OpinionBefore</th>\n",
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       "      <th>FriendsAfter</th>\n",
       "      <th>NewFriends</th>\n",
       "      <th>DeletedFriends</th>\n",
       "      <th>FolloweesBefore</th>\n",
       "      <th>FolloweesAfter</th>\n",
       "      <th>NewFollowees</th>\n",
       "      <th>DeletedFollowees</th>\n",
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       "      <th>FriendsOpinionAfterAvg</th>\n",
       "      <th>FriendsOpinionBeforeStd</th>\n",
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       "      <th>18981</th>\n",
       "      <td>25</td>\n",
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       "      <td>0.49</td>\n",
       "      <td>384</td>\n",
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       "      <td>0.35</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  OpinionBefore  OpinionAfter  FriendsBefore  FriendsAfter  \\\n",
       "18981   25    1           0.52          0.49            384           417   \n",
       "\n",
       "       NewFriends  DeletedFriends  FolloweesBefore  FolloweesAfter  \\\n",
       "18981          35               2               52              52   \n",
       "\n",
       "       NewFollowees  DeletedFollowees  FriendsOpinionBeforeAvg  \\\n",
       "18981             7                 7                     0.35   \n",
       "\n",
       "       FriendsOpinionAfterAvg  FriendsOpinionBeforeStd  \n",
       "18981                    0.35                      0.2  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SecondLargestHub = X[ X['FriendsBefore'] < LargestHub ]['FriendsBefore'].max()\n",
    "\n",
    "X[ X['FriendsBefore'] == SecondLargestHub ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3dafc287-311c-49ae-a8e2-788b4d42b6ba",
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
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       "      <th>OpinionBefore</th>\n",
       "      <th>OpinionAfter</th>\n",
       "      <th>FriendsBefore</th>\n",
       "      <th>FriendsAfter</th>\n",
       "      <th>NewFriends</th>\n",
       "      <th>DeletedFriends</th>\n",
       "      <th>FolloweesBefore</th>\n",
       "      <th>FolloweesAfter</th>\n",
       "      <th>NewFollowees</th>\n",
       "      <th>DeletedFollowees</th>\n",
       "      <th>FriendsOpinionBeforeAvg</th>\n",
       "      <th>FriendsOpinionAfterAvg</th>\n",
       "      <th>FriendsOpinionBeforeStd</th>\n",
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       "      <th>24335</th>\n",
       "      <td>35</td>\n",
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       "      <td>348</td>\n",
       "      <td>380</td>\n",
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       "      <td>133</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.17</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  OpinionBefore  OpinionAfter  FriendsBefore  FriendsAfter  \\\n",
       "24335   35    1           0.47          0.46            348           380   \n",
       "\n",
       "       NewFriends  DeletedFriends  FolloweesBefore  FolloweesAfter  \\\n",
       "24335          38               6               81             133   \n",
       "\n",
       "       NewFollowees  DeletedFollowees  FriendsOpinionBeforeAvg  \\\n",
       "24335            56                 4                     0.31   \n",
       "\n",
       "       FriendsOpinionAfterAvg  FriendsOpinionBeforeStd  \n",
       "24335                    0.32                     0.17  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ThirdLargestHub = X[ X['FriendsBefore'] < SecondLargestHub ]['FriendsBefore'].max()\n",
    "\n",
    "X[ X['FriendsBefore'] == ThirdLargestHub ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed0f756-a166-4294-81f1-05baac6d3a3d",
   "metadata": {},
   "source": [
    "# - Hubs attract new friends!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cef417-bfa9-4de1-8476-b0e65f442f15",
   "metadata": {},
   "source": [
    "# Plot degree distributions (Figure 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26a77db2-3761-460d-979a-59b21b0814f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 13), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "BackGroundColor = 'white'\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 1) \n",
    "sub.set_title('A. Number of friends $ t_{1} $', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X['FriendsBefore'], bins=21, color='royalblue', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "\n",
    "sub.set_xscale('log')\n",
    "sub.set_yscale('log')\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.set_xlabel('number of friends', fontsize = AxisSize)\n",
    "sub.set_ylabel('count', fontsize = AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 2) \n",
    "sub.set_title('B. Number of friends $ t_{2} $', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X['FriendsAfter'], bins=21, color='limegreen', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "\n",
    "sub.set_xscale('log')\n",
    "sub.set_yscale('log')\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.set_xlabel('number of friends', fontsize = AxisSize)\n",
    "sub.set_ylabel('count', fontsize = AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 3) \n",
    "sub.set_title('C. New friends', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X['NewFriends'], bins=21, color='limegreen', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.set_xscale('log')\n",
    "sub.set_yscale('log')\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.set_xlabel('number of new ties', fontsize = AxisSize)\n",
    "sub.set_ylabel('count', fontsize = AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 4)\n",
    "sub.set_title('D. Deleted friends', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X['DeletedFriends'], bins=21, color='royalblue', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.set_xscale('log')\n",
    "sub.set_yscale('log')\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.set_xlabel('number of deleted ties', fontsize = AxisSize)\n",
    "sub.set_ylabel('count', fontsize = AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "################################################################################\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0616ade4-3929-4aa9-a9a2-7b84d8e0e8d5",
   "metadata": {},
   "source": [
    "# Other network properties (times $ t_{1} $ and $ t_{2} $)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76b67614-7c34-486a-afe9-d9c50fbc806e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.001\n",
      "\n",
      "Transitivity: 0.07\n",
      "\n",
      "Avg clustering: 0.11\n",
      "\n",
      "Degree assortativity: 0.115\n",
      "\n",
      "Opinion assortativity: 0.226\n",
      "\n",
      "age assortativity: 0.493\n",
      "\n",
      "gender assortativity: 0.359\n"
     ]
    }
   ],
   "source": [
    "G = nx.from_scipy_sparse_array(A1)\n",
    "\n",
    "RoundValue = 3\n",
    "\n",
    "print(f'Density: {round(nx.density(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Transitivity: {round(nx.transitivity(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Avg clustering: {round(nx.average_clustering(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Degree assortativity: {round(nx.degree_assortativity_coefficient(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['OpinionBefore'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'opinion')\n",
    "\n",
    "OpAssort = round(nx.numeric_assortativity_coefficient(G, 'opinion'), RoundValue)\n",
    "\n",
    "print(f'Opinion assortativity: {OpAssort}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['age'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'age')\n",
    "\n",
    "AgeAssort = round(nx.numeric_assortativity_coefficient(G, 'age'), RoundValue)\n",
    "\n",
    "print(f'age assortativity: {AgeAssort}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['sex'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'sex')\n",
    "\n",
    "SexAssort = round(nx.attribute_assortativity_coefficient(G, 'sex'), RoundValue)\n",
    "\n",
    "print(f'gender assortativity: {SexAssort}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55e8f887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.001\n",
      "\n",
      "Transitivity: 0.071\n",
      "\n",
      "Avg clustering: 0.11\n",
      "\n",
      "Degree assortativity: 0.117\n",
      "\n",
      "Opinion assortativity: 0.233\n",
      "\n",
      "age assortativity: 0.485\n",
      "\n",
      "gender assortativity: 0.362\n"
     ]
    }
   ],
   "source": [
    "G = nx.from_scipy_sparse_array(A2)\n",
    "\n",
    "RoundValue = 3\n",
    "\n",
    "print(f'Density: {round(nx.density(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Transitivity: {round(nx.transitivity(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Avg clustering: {round(nx.average_clustering(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Degree assortativity: {round(nx.degree_assortativity_coefficient(G), RoundValue)}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['OpinionAfter'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'opinion')\n",
    "\n",
    "OpAssort = round(nx.numeric_assortativity_coefficient(G, 'opinion'), RoundValue)\n",
    "\n",
    "print(f'Opinion assortativity: {OpAssort}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['age'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'age')\n",
    "\n",
    "AgeAssort = round(nx.numeric_assortativity_coefficient(G, 'age'), RoundValue)\n",
    "\n",
    "print(f'age assortativity: {AgeAssort}')\n",
    "\n",
    "print('')\n",
    "\n",
    "labels = np.array(X['sex'])\n",
    "labels_new = np.around(labels*10, decimals=1)\n",
    "labels_new = labels_new.astype(int)\n",
    "opinion_dict = {i: labels_new[i] for i in range(len(labels_new))}\n",
    "nx.set_node_attributes(G, opinion_dict, 'sex')\n",
    "\n",
    "SexAssort = round(nx.attribute_assortativity_coefficient(G, 'sex'), RoundValue)\n",
    "\n",
    "print(f'gender assortativity: {SexAssort}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebc0ae1-5c5b-483f-b6eb-bb22c20241ad",
   "metadata": {},
   "source": [
    "# A slight increase in the assortativity coefficient. Transitivity is also slightly going up. Degree assortativity is on the rise. The clustering coefficient does not change. All changes are tiny."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3562625a",
   "metadata": {},
   "source": [
    "# Nodal characteristics (Figure 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c447267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 14))\n",
    "\n",
    "\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 1) \n",
    "sub.set_title('A. Age structure', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X['age'], color='darkorange', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "#sub.set_xlabel('age', fontsize = AxisSize)\n",
    "sub.set_ylabel('count', fontsize = AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "\n",
    "################################################################################\n",
    "\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 2) \n",
    "sub.set_title('B. Ideological composition', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sub.hist(X['OpinionBefore'], color='red', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "#sub.set_xlabel('opinion', fontsize=AxisSize)\n",
    "sub.xaxis.set_ticks([0.05, 0.5, 0.95], labels=['conservative', 'moderate', 'liberal'])\n",
    "sub.set_ylabel('count', fontsize=AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "\n",
    "################################################################################\n",
    "\n",
    "\n",
    "sub = fig.add_subplot(2, 2, (3, 4))\n",
    "sub.set_title('C. Gender structure', fontsize = TitleSize)\n",
    "\n",
    "sub.hist(X[X['sex'] > 0]['sex'], color='royalblue', edgecolor='k', linewidth=2.5)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks([1.05, 1.95], labels=['female', 'male'])\n",
    "sub.set_ylabel('count', fontsize=AxisSize)\n",
    "sub.set(facecolor = BackGroundColor)\n",
    "\n",
    "\n",
    "################################################################################\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "sub = fig.add_subplot(2, 2, 4)\n",
    "sns.histplot(data=X, x=\"sex\", stat='probability', ax=sub, color='royalblue', alpha=0.5, )\n",
    "sub.set_xlim([0.75, 2.25])\n",
    "sub.set_xlabel('gender', fontsize=fontsize_value)\n",
    "sub.set_ylabel('', fontsize=fontsize_value)\n",
    "sub.xaxis.set_ticks([1, 2], labels=['female', 'male'])\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "af5ab18c-8951-4925-ac44-e587ff7d352c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06654463228168407\n",
      "\n",
      "0.1329098022325114\n"
     ]
    }
   ],
   "source": [
    "print(sum(abs(X['OpinionBefore'] - 0.5) > 0.45) / N)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(sum(abs(X['OpinionBefore'] - 0.5) > 0.4) / N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4e2528eb-db79-4e10-b7f2-fff991306779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "387557042.5\n",
      "\n",
      "0.767747860343595\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X['OpinionBefore'], X['OpinionAfter'], alternative=\"two-sided\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb3f5a6-c3bc-460b-9d29-0b3c9f3dabb8",
   "metadata": {},
   "source": [
    "# No significant differences in the public opinion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90de4a7-15a3-48a8-8f92-76ccf1fd74c3",
   "metadata": {},
   "source": [
    "# Let us introduce ideological groups and analyze how properties of individuals vary across ideological groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "b421ca61-ea39-481c-8ca4-51d619423786",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################################\n",
      "\n",
      "Number of friends\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 13, mean value: 16.55\n",
      "\n",
      "Ideological group: Conservatives, median value: 11, mean value: 14.92\n",
      "\n",
      "Ideological group: Moderates, median value: 12, mean value: 16.94\n",
      "\n",
      "Ideological group: Liberals, median value: 13, mean value: 18.66\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 15, mean value: 20.69\n",
      "\n",
      "########################################\n",
      "\n",
      "Number of new friends\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 1, mean value: 1.41\n",
      "\n",
      "Ideological group: Conservatives, median value: 1, mean value: 1.29\n",
      "\n",
      "Ideological group: Moderates, median value: 1, mean value: 1.63\n",
      "\n",
      "Ideological group: Liberals, median value: 1, mean value: 1.6\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 1, mean value: 1.53\n",
      "\n",
      "########################################\n",
      "\n",
      "Number of deleted friends\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 0, mean value: 0.31\n",
      "\n",
      "Ideological group: Conservatives, median value: 0, mean value: 0.26\n",
      "\n",
      "Ideological group: Moderates, median value: 0, mean value: 0.34\n",
      "\n",
      "Ideological group: Liberals, median value: 0, mean value: 0.49\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 0, mean value: 0.57\n",
      "\n",
      "########################################\n",
      "\n",
      "Number of followees\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 93, mean value: 96.28\n",
      "\n",
      "Ideological group: Conservatives, median value: 45, mean value: 54.6\n",
      "\n",
      "Ideological group: Moderates, median value: 29, mean value: 40.34\n",
      "\n",
      "Ideological group: Liberals, median value: 51, mean value: 61.49\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 73, mean value: 80.25\n",
      "\n",
      "########################################\n",
      "\n",
      "Number of new followees\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 6, mean value: 8.97\n",
      "\n",
      "Ideological group: Conservatives, median value: 4, mean value: 7.05\n",
      "\n",
      "Ideological group: Moderates, median value: 3, mean value: 6.15\n",
      "\n",
      "Ideological group: Liberals, median value: 5, mean value: 7.73\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 7, mean value: 9.69\n",
      "\n",
      "########################################\n",
      "\n",
      "Number of deleted followees\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 4, mean value: 6.26\n",
      "\n",
      "Ideological group: Conservatives, median value: 2, mean value: 4.25\n",
      "\n",
      "Ideological group: Moderates, median value: 2, mean value: 3.91\n",
      "\n",
      "Ideological group: Liberals, median value: 3, mean value: 6.62\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 5, mean value: 9.44\n",
      "\n",
      "########################################\n",
      "\n",
      "Age\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 36, mean value: 38.13\n",
      "\n",
      "Ideological group: Conservatives, median value: 37, mean value: 39.79\n",
      "\n",
      "Ideological group: Moderates, median value: 35, mean value: 36.77\n",
      "\n",
      "Ideological group: Liberals, median value: 28, mean value: 30.11\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 26, mean value: 28.67\n",
      "\n",
      "########################################\n",
      "\n",
      "Gender\n",
      "\n",
      "Ideological group: Strong conservatives, median value: 2, mean value: 1.54\n",
      "\n",
      "Ideological group: Conservatives, median value: 1, mean value: 1.44\n",
      "\n",
      "Ideological group: Moderates, median value: 1, mean value: 1.49\n",
      "\n",
      "Ideological group: Liberals, median value: 2, mean value: 1.51\n",
      "\n",
      "Ideological group: StrongLiberals, median value: 2, mean value: 1.56\n",
      "\n",
      "########################################\n"
     ]
    }
   ],
   "source": [
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of friends')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['FriendsBefore'].median())\n",
    "    FriendsMean = round(X[mask]['FriendsBefore'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of new friends')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['NewFriends'].median())\n",
    "    FriendsMean = round(X[mask]['NewFriends'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of deleted friends')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['DeletedFriends'].median())\n",
    "    FriendsMean = round(X[mask]['DeletedFriends'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of followees')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['FolloweesBefore'].median())\n",
    "    FriendsMean = round(X[mask]['FolloweesBefore'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of new followees')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['NewFollowees'].median())\n",
    "    FriendsMean = round(X[mask]['NewFollowees'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Number of deleted followees')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['DeletedFollowees'].median())\n",
    "    FriendsMean = round(X[mask]['DeletedFollowees'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Age')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['age'].median())\n",
    "    FriendsMean = round(X[mask]['age'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Gender')\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    maskName = OpinionMaskNames[i]\n",
    "    \n",
    "    FriendsMedian = int(X[mask]['sex'].median())\n",
    "    FriendsMean = round(X[mask]['sex'].mean(), 2)\n",
    "    \n",
    "    print(f'Ideological group: {maskName}, median value: {FriendsMedian}, mean value: {FriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6f3ef3-4bf1-48b5-9c8c-091b7a3a4367",
   "metadata": {},
   "source": [
    "# Let apply statistical tests "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "d66527a4-9bb4-42e1-a029-90adbd904604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3609078.5\n",
      "\n",
      "1.21502660398829e-07\n",
      "\n",
      "4504855.5\n",
      "\n",
      "1.39623647324568e-22\n",
      "\n",
      "4149313.5\n",
      "\n",
      "7.291164289941751e-14\n",
      "\n",
      "1147883.5\n",
      "\n",
      "0.004840890891607727\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#OpinionMasksBefore = [MaskStrongConsBefore, \n",
    "#                      MaskModConsBefore, \n",
    "#                      MaskModBefore, \n",
    "#                      MaskModLibBefore, \n",
    "#                      MaskStrongLibBefore]\n",
    "\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskStrongConsBefore]['FriendsBefore'], \n",
    "                    X[MaskStrongLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModConsBefore]['FriendsBefore'], \n",
    "                    X[MaskStrongLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['FriendsBefore'], \n",
    "                    X[MaskStrongLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['FriendsBefore'], \n",
    "                    X[MaskStrongLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d94dc846-9f29-48f0-992f-89e3184e937e",
   "metadata": {},
   "source": [
    "# - Strong Liberals tend to have more friends than others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "bee0b613-b412-4df3-803b-07a189be7fd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6948486.0\n",
      "\n",
      "0.003206479400046134\n",
      "\n",
      "8680954.0\n",
      "\n",
      "4.680448010325181e-19\n",
      "\n",
      "7974357.0\n",
      "\n",
      "2.5041329631126704e-09\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#OpinionMasksBefore = [MaskStrongConsBefore, \n",
    "#                      MaskModConsBefore, \n",
    "#                      MaskModBefore, \n",
    "#                      MaskModLibBefore, \n",
    "#                      MaskStrongLibBefore]\n",
    "\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskStrongConsBefore]['FriendsBefore'], \n",
    "                    X[MaskModLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModConsBefore]['FriendsBefore'], \n",
    "                    X[MaskModLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['FriendsBefore'], \n",
    "                    X[MaskModLibBefore]['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d7d0e6-a429-458d-aab5-b743d190b40e",
   "metadata": {},
   "source": [
    "# - Liberals tend to have more friends than others (excepting Strong Liberals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "139e538c-d2e1-4a10-ad3d-5d9039431866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14418251.0\n",
      "\n",
      "0.0\n",
      "\n",
      "7905730.0\n",
      "\n",
      "0.0\n",
      "\n",
      "3898834.5\n",
      "\n",
      "2.887237636018349e-225\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongConsBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongConsBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongConsBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a8ecb3-c489-4476-b7de-3a5c23f7920c",
   "metadata": {},
   "source": [
    "# - Strong Conservatives tend to have more followees than less radical ideological groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "257b81d0-17cd-4055-8734-c031ef5beb3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3484013.0\n",
      "\n",
      "3.415761887343955e-90\n",
      "\n",
      "2001515.5\n",
      "\n",
      "7.6181246806540605e-227\n",
      "\n",
      "893120.5\n",
      "\n",
      "3.318744705438732e-36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongLibBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongLibBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['FolloweesBefore'], \n",
    "                    X[MaskStrongLibBefore]['FolloweesBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a880690-9212-402e-b4d7-43d82ee5b728",
   "metadata": {},
   "source": [
    "# - Strong Liberals tend to have more followees than less radical ideological groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "58839c85-0be4-4a5d-9e6f-9793ab2c8d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26570242.0\n",
      "\n",
      "3.066155097210762e-89\n",
      "\n",
      "20135203.5\n",
      "\n",
      "4.1461845847950294e-216\n",
      "\n",
      "6267602.5\n",
      "\n",
      "8.258045253000701e-21\n",
      "\n",
      "3910669.5\n",
      "\n",
      "0.14963389902003643\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['NewFollowees'], \n",
    "                    X[MaskStrongConsBefore]['NewFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['NewFollowees'], \n",
    "                    X[MaskStrongConsBefore]['NewFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['NewFollowees'], \n",
    "                    X[MaskStrongConsBefore]['NewFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskStrongConsBefore]['NewFollowees'], \n",
    "                    X[MaskStrongLibBefore]['NewFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "94cfa049-7ba4-40ac-b59c-96f08270d1b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24444746.5\n",
      "\n",
      "1.98410120919208e-164\n",
      "\n",
      "19368707.0\n",
      "\n",
      "2.6631203869649523e-259\n",
      "\n",
      "6920379.0\n",
      "\n",
      "0.001300712077211087\n",
      "\n",
      "3401019.0\n",
      "\n",
      "4.1904810864315103e-16\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['DeletedFollowees'], \n",
    "                    X[MaskStrongConsBefore]['DeletedFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['DeletedFollowees'], \n",
    "                    X[MaskStrongConsBefore]['DeletedFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['DeletedFollowees'], \n",
    "                    X[MaskStrongConsBefore]['DeletedFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "################################################################################################\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskStrongConsBefore]['DeletedFollowees'], \n",
    "                    X[MaskStrongLibBefore]['DeletedFollowees'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd1b0fb-b409-41eb-8e31-811a8a424bfe",
   "metadata": {},
   "source": [
    "# - Individuals with radical positions (Especially Strong Liberals) renew their followees more often"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "802ea2ea-77b0-43b4-b97c-c609c4bfadca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29292578.5\n",
      "\n",
      "5.5598970888275194e-36\n",
      "\n",
      "27039534.0\n",
      "\n",
      "6.91986049446713e-11\n",
      "\n",
      "7047164.0\n",
      "\n",
      "0.019903278667468313\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['sex'], \n",
    "                    X[MaskStrongConsBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['sex'], \n",
    "                    X[MaskStrongConsBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['sex'], \n",
    "                    X[MaskStrongConsBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "3fff3f44-78de-48dd-a5c8-e14d83b25dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4806190.0\n",
      "\n",
      "7.305554675770284e-15\n",
      "\n",
      "4441598.0\n",
      "\n",
      "1.7292964361722125e-06\n",
      "\n",
      "1158289.5\n",
      "\n",
      "0.005831208506658668\n",
      "\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskModConsBefore]['sex'], \n",
    "                    X[MaskStrongLibBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModBefore]['sex'], \n",
    "                    X[MaskStrongLibBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n",
    "U, p = mannwhitneyu(X[MaskModLibBefore]['sex'], \n",
    "                    X[MaskStrongLibBefore]['sex'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)\n",
    "\n",
    "print('')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845eade0-e80c-4a71-9754-0e8e9f5c9973",
   "metadata": {},
   "source": [
    "# Opinion homophily structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0d03f3-e556-4265-89e1-3418a964d4e6",
   "metadata": {},
   "source": [
    "# At time $ t_{1} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "43d902f0-665b-4605-89ad-22f2f7ad62c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "NullModelBefore = []\n",
    "\n",
    "for i in range(len(OpinionMasksBefore)):\n",
    "    \n",
    "    mask = OpinionMasksBefore[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBefore.append(sum(mask) / N)\n",
    "    \n",
    "NullModelBefore = np.array(NullModelBefore)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in OpinionMasksBefore:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBefore = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) \n",
    "\n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "\n",
    "        Composition_atomic = []\n",
    "\n",
    "        for Group in IdGroups:\n",
    "\n",
    "            Intersection = Group & FriendsBefore\n",
    "\n",
    "            Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "            \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBefore.append(Composition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ff9f4035-0c7b-45af-b53b-f4c17c2e0f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "(5,)\n",
      "(5,)\n",
      "(5,)\n",
      "(5,)\n"
     ]
    }
   ],
   "source": [
    "for i in CompositionsBefore:\n",
    "    \n",
    "    print(i.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb65a46-a312-4af8-9217-7c8366364652",
   "metadata": {},
   "source": [
    "# At time $ t_{2} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4959a82a-709a-456b-a0e2-00c730fcc7d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = X.shape[0]\n",
    "\n",
    "NullModelAfter = []\n",
    "\n",
    "for i in range(len(OpinionMasksAfter)):\n",
    "    \n",
    "    mask = OpinionMasksAfter[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelAfter.append(sum(mask) / N)\n",
    "    \n",
    "NullModelAfter = np.array(NullModelAfter)\n",
    "\n",
    "#NullModelAfter = np.round(NullModelAfter, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in OpinionMasksAfter:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsAfter = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsAfter = set(A2.indices[A2.indptr[member]:A2.indptr[member+1]]) \n",
    "\n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsAftereNumber = len(FriendsAfter)\n",
    "        \n",
    "        if FriendsAftereNumber > 0:  # Some people do not have friends at time t2\n",
    "\n",
    "            Composition_atomic = []\n",
    "\n",
    "            for Group in IdGroups:\n",
    "\n",
    "                Intersection = Group & FriendsAfter\n",
    "\n",
    "                Composition_atomic.append(len(Intersection) /  FriendsAftereNumber)\n",
    "\n",
    "                    #Composition_atomic.append(None) \n",
    "\n",
    "            Composition.append(Composition_atomic)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            pass\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsAfter.append(Composition)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a670cb73-cc03-4111-9f52-fd998e5520c9",
   "metadata": {},
   "source": [
    "# Opinion homophily after controlling for age (time $ t_{1} $)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208ad016-8ea2-4297-9d81-917d88aabc4a",
   "metadata": {},
   "source": [
    "# Among young individuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f91d1bd7-9ca0-4b1e-807d-c8e40500d726",
   "metadata": {},
   "outputs": [],
   "source": [
    "MaskAge = ( 0 <= X['age'] ) & ( X['age'] <= 20 )\n",
    "\n",
    "YoungUsers = set(np.arange(N)[MaskAge])\n",
    "\n",
    "masks = [MaskStrongConsBefore & MaskAge, \n",
    "         MaskModConsBefore & MaskAge, \n",
    "         MaskModBefore & MaskAge, \n",
    "         MaskModLibBefore & MaskAge, \n",
    "         MaskStrongLibBefore & MaskAge]\n",
    "\n",
    "N = X.shape[0]\n",
    "n = sum(MaskAge)\n",
    "\n",
    "NullModelBeforeYoung = []\n",
    "\n",
    "for i in range(len(masks)):\n",
    "    \n",
    "    mask = masks[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBeforeYoung.append(sum(mask) / n)\n",
    "    \n",
    "NullModelBeforeYoung = np.array(NullModelBeforeYoung)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in masks:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBeforeYoung = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) & YoungUsers\n",
    "        \n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "        \n",
    "        if FriendsBeforeNumber  > 0:\n",
    "            \n",
    "            Composition_atomic = []\n",
    "\n",
    "            for Group in IdGroups:\n",
    "\n",
    "                Intersection = Group & FriendsBefore\n",
    "\n",
    "                Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            pass\n",
    "                \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBeforeYoung.append(Composition)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdcb921f-304f-44d0-b863-362b0c6584d1",
   "metadata": {},
   "source": [
    "# Among elder individuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "025dd9dd-3fb3-460e-8d0b-3ee379b19424",
   "metadata": {},
   "outputs": [],
   "source": [
    "MaskAge = ( 50 <= X['age'] ) & ( X['age'] <= 100 )\n",
    "\n",
    "OldUsers = set(np.arange(N)[MaskAge])\n",
    "\n",
    "masks = [MaskStrongConsBefore & MaskAge, \n",
    "         MaskModConsBefore & MaskAge, \n",
    "         MaskModBefore & MaskAge, \n",
    "         MaskModLibBefore & MaskAge, \n",
    "         MaskStrongLibBefore & MaskAge]\n",
    "\n",
    "N = X.shape[0]\n",
    "n = sum(MaskAge)\n",
    "\n",
    "NullModelBeforeOld = []\n",
    "\n",
    "for i in range(len(masks)):\n",
    "    \n",
    "    mask = masks[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBeforeOld.append(sum(mask) / n)\n",
    "    \n",
    "NullModelBeforeOld = np.array(NullModelBeforeOld)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in masks:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBeforeOld = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) & OldUsers\n",
    "        \n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "        \n",
    "        if FriendsBeforeNumber  > 0:\n",
    "            \n",
    "            Composition_atomic = []\n",
    "\n",
    "            for Group in IdGroups:\n",
    "\n",
    "                Intersection = Group & FriendsBefore\n",
    "\n",
    "                Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            pass\n",
    "                \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBeforeOld.append(Composition)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a57187-d0fe-4329-b156-5633d94d8448",
   "metadata": {},
   "source": [
    "# Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "65b89cc2-658a-4212-9741-fcd67aaf2270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tZD15fe6VkXczokQ6xq83Uo+I9gT9uhe0DZPkfcZ3i1K+m7y+H1o++/cc71n/CMfG8TUZAGJCpo4NmdpDpg5Hpi4YMnVikanJ1Ps9BqgRpHT/Z4/QiWZ2sqReuS3knBsh7yYCZ8u7g255/R2wQw2S95+3y8ysXcS8+xX9tJd4vev/fNTMKmVPNLMa/jrC+KdLviTvv4ovmllSZBkzq29mrUImvZPdZv/LQ3a5spL6ynsfvx1DW9+RZJKe9pfNrqeWpAci1iVJ7/s//xN67TD/+oSPxbC+bN/Ie63PMrNLImeaWeg14tL9nz0iyjSX9GQc64zKObda0ify9n/fyBBgZlWzt9U5t0TezRzS5F3HMLTckfLuNr9O3imyRe0BM9t73S+/rz3uP303pNyH8k7dvNUPhqEelpQi7wYj2/16DsvlunDZ/6neEkPb1kqqHa0vR+Of1vWRpGS/TXuZ2QGS/ulvwwex1FcAiXo/JZRzbpW8/XK4mT0Q+h7NZmYHmFmznEuHGSHvi/OxZhb5BfZ6edfsTKSDJN0UOsHMzpYXJufJO9W8OLzuBW1Ddn953Mwqh5SvLOkJ/2lof/lIf78H00LKl5H0tMhdAIpGuv+zR+hEMjWZWmTqSGTqxCFTJxaZmky93+Ma1AjSq5KulvSZmX0u74YXreVdt+tTSRflsez78j4MH5B3TaSPIgs45zaY2c3yPhhHmtmn8q6HdJS8u/cOk/eBH+1mBLH62G/nWZKmm9kgeeH+fEnjJB0QZZmH/fXfIOlMM/tN3vXb6sj7w3S0vLvWzvS3Y6SZPSXpLn8dn8u7a+6p8vbXcHkf0vnp6y9ztqQpZvadvJsbXOCv+ynn3N6bQDjnhpnZG/L+AM8wsy/k/ZE4U96XlGWKYd8553aY2QXyroc4wMx6yzsCpJK8m1Mcr78/i76R9wf4X2bWRt5/WpvIuybiEP/3wrpF3n67QVIPM/tR0g55d4E+Wd5rOdQve4O8cPK0mZ0k78YRjeXtsz2SrvaPpClqf8l7DT6X9xqcLa9vDVHIH37nXLqZ3S7pFUkT/T6/Wl4/7yLvaKO7Q+q9QlJvMxsuL4Ct8+s9U9J2Sc/H0LZf5V3f7Qcz+8Nfbopz7ps8lrlH3lENt5jZEZJ+l3da14Xygs4tzrmFMaw7bgl8PxWFW+R9Bjwk6Qr/dVkpqYG898oR8o7EyXXfOOf2mFkvST9IGuy/b+dLaivvFO7v5W1rYT73Qv0g6Rk/uE+Rd8TWefKOKrrGhd/sJbDXvaBtcM4N8L8cXCjvPfi1vNMjz5H3mTHQOfdRSPl0M7tH0jOSJpnZQHmflyfLG8CZKu+1AIBEIlOTqcnUsSFTJwiZmkwdTxvI1IiJc44Hj8Ae8oLtb/L+iG+U94fsHHn/7XeS+uSyXBN5p0E5Sd/ks45T5d1ld4u/nkHy7rj8rb98aoxtHeq9ZXJMryDpv5IWyAsS6ZIelXfjESdpaJRlTF6Q+VVSprwwt9Tf/vskNY6yzMX+/I3y/lDNkBe6K0Upmy4pPcr0Sn790+Xd2Tx7n1+SyzaXkXSHvBC2XV6AfkXeTQs2SpocUb6nv809c3nNXpUXBHbIO0pgjKT7Iso1lvflaKnfxhnygk+5aPtT3s1InKQecfS7Kv6+m+r3i43yvrw8L6lORNmGkvpJWuS3e42kryUdEaXevLY/1z4t74gSJ+86gTn6nN+XHvH33Xa/rz0oqWIu23eSvC8v6/zy8yQ9pYi+LulIf9um+P1wq1/2XUmtY9k2f1/2k3en7l2R2xHtNfOnp8o7gmeu38b18o6uOSmefZdXf8+nD8TzfipIH8u1L+SzXyrIC9Uj5YWw7ZIy5H1W3C6pZox96kh/f270H7/I+0L1sr9M+1ja48/r789Pi7Zuv95f5F03bqPf93K8P4rgdS9Qv4inDX75MvKOaBkv7/Nii6QJkm6WVCaXZS6Rd2riNnlfaD+U96VoqKL8HeHBgwePwj5EpiZTk6klMjWZOvzzhEwd2+teoH4RTxv88mRqHnk+zH/Rgf2Kf6rPAkkVnHP18yuPcOZdc2+OpE+cczlOMURimNlQSd2dcxZ0W1A6mNkIeUG7mnNuc9DtAQCUbGTqwiFT7xtkaiQamRpIPK7bglLNzFJDr3HkTzN517Jron1zrbMSy8zqRbmmXGX9fZoa+w8oZsysskW5YYmZ9ZR3hN1PBGkAQDzI1IVDpgZKHjI1sG9xDWqUdp0lDTSzn+SdmlLVn9Ze0mJ5p9Igd7dLusQ/6mC5pHryrm/XSN51tz4LrGUActNE3rXafpZ3emk5SR3k3YV8vaQ7g2saAKCEIlMXzu0iUwMlDZka2IcYoEZpN1vedfGOlnSavD6/RNKLkh5z3l1+kbuf5d185iRJNeRdD22OvP33vOMaQUBxtFLeNSe7SzpW3vUWV8i7DuKjzrn5AbYNAFAykakLh0wNlDxkamAf4hrUAAAAAAAAAIBAcA1qAAAAAAAAAEAgGKAGAAAAAAAAAASCAWqghDKzY83MmdmFQbeluDGzdDNLj5jW099fPYNpVTgzG2pmMV9jKdo2FRf+fh0adDuKUrRtNLM+/vQegTQqgRK1LWbW368nLWTaYf60XoVsJgAACUWezh15et8iT1uPQBqVQORpoHAYoAZKIDMrI+k5SVPEXb9RxIpzmEfx55ybIOlrSQ+bWdWAmwMAgCTyNPYt8jQKgzyN/QED1EDJdLG8O4E/xV2/Y/aVpEP8nwD2rccl1ZP0z6AbAgCAjzwdP/I0EBzyNEo1BqiBkulmSRtEOIyZcy7LOTfLOZcVdFuA/Y1zbqykWZJ6+0esAQAQNPJ0nMjTQHDI0yjt6NRACWNmLSUdJWmwc25rlPnp/qOKmT1tZhlmtt3M5pnZ3WZmudR7oZn9YWZZZrbVzKaZ2b1mVjGPdaSY2bP+7zvNrE/E/Kpm9pyZLfbrnGxm5/hlypnZf8xsrpltM7P5ZnZLlHVVMLNbzOw7M1vkb0ummf1iZqfGsd9yXDMv5PpeuT3So9RziZn9bmbr/Xb/ZWb3R9tPfvmLzWyCv/2rzOwDM2sQa7uj1FfNzF42s6X++mea2T9DX1cza+m3//c86pnmv2b18yjTw7+uX1NJTSP2Tf8o5WuZ2Rtmttx/nWaY2dV51H+y/7qu8cvP9/tsahz7Y++13szsfDMba2Zb/D7yiZk1jLJMrqdYWhFeBy+kvzXz+/RM/zVMN7P7sl9DM7vA347Nfp952cyScqnzeDP7wd/e7WY2x8yeMLNquZQ/zC+/0cw2+O+jLvm0u6Xf9sVmtsPMVprZADM7OM5d8ImkJpJOjHM5AAASysjT5GnydGgd5GnyNBC4ckE3AEDcTvB/Ds+jTHlJP0pqIOl7SbsknSPpCUmVJP0vtLCZPSbpXklrJA2QtEnSqZIek3SymZ3knNsRsY4Kkn6TVEPST/KOQFkY0Yaf/fmD/PKXSPrCzE6SdJOkI/32bZd0gaSXzGy1c25gSD01JL0gaaRf32pJ9SWdKek7M7vOOfdWHvsiL19LSo8yvY2k8yRtCZ1oZu9IulrSEklfSFovqbOkhyUdb2YnOud2hZS/Q9Kzfrn3/Z8n+9tSkCNPKkj6RVKqvHBSQdI/5O2fg+UdCSTn3Cw/TB9rZi2cc3MituMoSa0lfeGcW57H+tLl9ZXb/efPh8ybHFE2VdIISTskfS6porzX9B0z2+Ocey+iDQ9K6iMpU9K3klZJaivp35JOM7MuzrkNebQt0k2SzpI0WNIweX3rIkntzKy9c257HHUVtb6Sekj6Rt575yxJj0qqYGaZ8t6nX0v6U174vFlSWUk3hlZiZr0l9ZO0Wd61M1f59d4t6UwzO9o5tz6k/FHy+k8FSV9KmiepvaSh8t7LOZjZKX7Z8n5750lqJO/9cbqZHeucmxjjdo/wf54o7/MJAICgkKfJ06kiT0ciT5OngeA453jw4FGCHvKClJN0WC7z0/3530lKCpleR16gWy+pfMj0Ln75DEn1QqaXk/cH1Em6L5d1/CKpSh5t+EZSxZDpXf3pmZLGSUoNmddcXhibFFFXRUmNoqyjmqTpfl1JUdafHjGtp7/unvns30byAvNWSZ2jLP9llPX18efdFjItzd+eTElpIdPLyAvjzvsIjvl1z96nwyP2aQ1J8/153UKmn+9P6xulrv7+vBPjWHd6HvOd/3hLUtmQ6a3kfZmbGVH+WL/8yNA+ELGfn4uxbdn7foOkNhHzBvjzLox1e0Lq6xFlG4fGUjaPtmbv93RJDUOmp8r7MrtZ3hfGQyL6/0x5XzrrhExv6k/bIKllxHpe9dfzRsg0k3dKoJN0dkT520Jewx4h06tLWue3rVXEMq3lffGemMs2pkXZ/mr+vLGx9nsePHjw4MGjKB4iT2dPJ0878rTI0+RpHjyKwYNLfAAlTxP/Z17/qZekf7qQUxadc6vkHXlRTd7RAdmu8X8+4pxbEVJ+l6Q7Je2R1CuXddzpnNucRxtudyH/aXfO/SnvqJDqku52If+Nds4tkPcf4dZmVjZk+nbn3JLIip137bt3/LqOyKMNMTOzZElD5B0pc4VzbnTI7NvkhcNrXM5TQR+WtFbSZSHTLpP3X/KXnHPpIe3eI+n/5O3Xgrg3Yp9m+uuXvKNRsn0tr4/0tJDTJf3T/S6UF8J/KWAbotki6V/Oud0hbZsp7zU9xMLvNp19Y4/rQvuAv0x/eUeThO7LWLzonJsWMe1N/2enOOsqag8755ZmP/H3wWBJlSX1c879FTJvu6SB8o7SOCSkjsv9aS8752ZF1P8fSRslXRHy2h8l733/h3NuUET5l+X1h0hXygv7D/qv5V7Oueny9m8HM2uV3wb7y2RJ2qa/P8MAAAgKeVrk6ZD6yNMe8vTfyNPAPsYlPoCSp6b/c10eZbKcc/OiTF/s/6weMq2j/zPHKUnOuTlmtkRSMzOr5sJviLJN0tQ82rDeORftj/QySc0kTYgyb6m8z6V6/u+SJDM7VF4I7Sb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9P4ueeSyz8XImK/Klc8D9HPzL0FtB+1znvh/+76v/+/R9FK9fWk4vKl86B910D/ot+1JSqRD7ZpYPvzR8+/vnv68kE2n/EOv+cte9LGesRl/Z2Rr0vr8Y5rh8+3ctwGPu4bd/RlC+Ps9jecjvccfq2Pr7vfY2v333y5kMt4ykP5RVd+zw22anpGZh8neSpFV+2+4OSn+PpKty+C7fKmdMRCtpl5y60T+980Psm+q3zUVyxkf15XWP37qtkk7OYf/U3KyTc9eQ73tweoTP/G53my2S4qIoK+2CysTWoPfSShoiqUQO+16grHPHFgV+d79yt+8ip24KVfe9XsBlMervmZyxbv1f+1DQa3+pXJzfcviOho19JFX2e09vCFpXW9Icv7ztC3pPD0p6IIfvQLtc5C9U3efbP68xz7GS1vhtu0dZddNeSZdEyqOkKpL+DvoMg8vTT5JKh9h3ubs+YtxypP9J6up7L3O5/dV+7/1tBZy3VL/X6pyL7fNbXonRidF9f8TogfsTo+etPOT3uInRidH9lxGjZ9XZMYnR/V7j8aBj2Snnu+B7niapSYj9OivyOfO2oHS3+KX7n6SHctj/7KD3aZ8Cv597Jd2cQ5mIeE6M1V9MEzscf5I6+r1JF3mdH798pfrlq3Mutvdtmy7pX0nt5fRoN5JOlTTfV8gUugIZ6K5fIucWyUru8rKSLnWXW0k/RHkcp/t92R+XVMVvXVU5AWV/SbWC9svVl1lZwe92OSeRy+VWQJLqSCqX2/QifZHljD/uq1Tnyqlkfa9zlKRmcsYtPytM/sJ+hopt8Ltd0q+Sjvdbf4z7WF5OhWPlBKltJZVx18XLqYh8J9//y0OZ/VZZFdIDfu99DUmf+eXx7ry+Bzm8/tvKOjm8JCnBXV5Z0qt+r5/tBOdXPra4+z+rrO9AkqQP/fZ/MML+2cqXsoLAdDf9W+QGBm4Z/VlZ35FjIuzftYCOOd39zB7yO+aqkj71279DHj6P/B53LI4tQ05Qeaffax8naZq7fpn72W6WdI2cMRaNnO/zYnebcSHSryhpqbt+taQL5darkk6W01Bg5QSjJ0f4Lm+RtELOOKrGXddC0mxlnazrBO2b6rd/uqRxkpq760rKCRjS3PVjQry2//6pUawbpghBmrvNdHebD6MsKy0kfeLmvZLf8iqSHlTWBVOo7147vzxnyAkUU/w+p9f81j8pJxD6QFI1v/LUT1ll8djC9D1z8+xb/46kqu7ySnLG1PQFwyHrnxze9+vc/XZIqhhmmwfcbTbJPV+4y4+SNElZ5biT3MZYSfUlDfV7zy6I8B1ol4s6JFTd5/8diCrmkfM9933HNkq6QtJR7rrjJY1UYINMu6D9j/LL20w5F9G+8115STcr66L0vRB5X65cxC1H+p+ib6CuIOfCNmI9FaO8pfqVj8652D7P5dXdnxidGJ0YnRidGJ0YPdw6YvRiFqO76dyurHPH05JquMt957WR7vpVkioE7dtZ4c+ZTeX8COQ73xzvLi8lp200Q1mNz6H2byTnu2sl9ZZ0grLi6BRJH7nr9sst/2HKRNhzYiz/YprY4fiT88uh702q5XV+/PKV6pevzrnY3rftBt+XOkRB8m3TJmjdGe7y9ZKSw6RfR1m/ipwSxXE87u7zR5TH76sw+uew3XJ3uwMK8etRNOnl8EUe665bKCk+iuPw5S/sZ6jYBr+TfRVEiP2fc7f5SyF+RXS3uUJZF+0lozjOln55uDPMNt/6pV02L+9BhNevrayK9rUw27zjrt8nqWaY8mElvRRmf98F4uYQ+Q9bvpQVBFpJ7UOsL6OsnnzPRNi/awEec8jyqawg8dM8fCZ5Pu4YH1unEPs28FtvFaLHgaQOfuuDA9An/F67YYh9K8oJrK2kX0Ks96W7V9IJIdZXc8uZlfRR0LpUv/3/U4heEArs/RkpeE6NYt1l7vJdcoO/oPXN/PZtlJfvcYSy5OsZuTjEunZ+rztcoXtPjfHbJltZlhPo+S5mni0s3zM5DRa+wD9kL2f5NeIp+gbqUspqSL0rzDa+C7F3gpZf5/e62Xp9yrkQ8zVgz4nwHWgXIX9/KUTdF7R/XmKeG5V1MXNGiH3LKquhKFseJd2krO9ffJi8N3PT3xucP+UybjnS/xRlA7W7z0J3n2yNFjHOW2pO3+ug7fNTXonRnW06ixidGJ0Y3X8bYnRidN+6y0SMXtxi9IrKaiQ+L8w2Jf3y97+gdZ0V/pz5m7tuQZgyep5fvkPt72sYD/meu9t0d7f5MUKZCHtOjOVfURyDuqrf/+me5SJ2eltrNwQvtNbOUdaM442DVt/uPg6y1q4Klah1xuUe7T49L4r8bHEfk4wz63pB+d1aO7MgEjbOJJqnu0+fttYWyrHHXW9Zaw+GWef7nN+11u4Ps82Pcm7bSZRzMsut69zH1ZL6hNnmOfcxUc6v0bF0lZxKeo+k18Ns84qcYKOUnJNpKLvl/BIbykvuYxXlLf/jrbWjgxdaa/fKuX1Nyv7djCRWx7xK0udh1v2ch3wFy8txx+rYVsq5vSr4tZfI6X0hSWOtteNC7Pu3m36o/PnK+7fW2rkh0t8u6U336QXGHfcyhG+stf+F2H+DnN4K/q8VyjvW2t0hlg+TE4hJTsNHLPwi5/sdJ6eBLtgd7uNE93wTS7+6jw2MMTUibPeGdaOfIH/4/d8teKVbZ450nxZUWczL9+xcOb0wJKcXSCjvyLkgiZp7HvjMfXpn8HpjTCtllZ/eQat95XKitXZ4iLQPSHrRfdrQGBOrchgsLzHPNe7jGGvt2BD77pH0VoTX9J1Le4aLB6y10+X0lC0tp7dsKAUWtxzBfHF8YR2Dmhg9xojRc4UYPWfE6H6I0SURo+cWMXoBxOhyji1B0kxr7R+hNnBj7a/cp7k69xtjEvy2fStUGXVfb2KY/VPl/EB0QOHrY0ka4D6eHSG+iHROjJmi2EBd3EyOsC7NfQwO3Nu4j7cbZ/D/kH9ybu2QsiYVyY2RciqNJpLGGmNuN8bUi2L/3BpfAGn6tHYfD8o5qRRmId8HY0xtZX1un0X4jNfKuU1Wiu5zbu4+jrbWHgq1gXuSXxO0faz40ptqrd0W5vUz5PzKGOn1p0XYf5Gck3+k/SPJy3czklgd89QwAUNe8xUsL8cdy88z3LGt971GmPQPyhnWQHJuE5PkTGCkrCDlzzBpS9II97GEnFupQhkVYX/fuqoR6syQ760bsGx0n8akocZ9Pz51n97hv84YU17ObedS9obMXDHOxGGPGWP+dide2ec3iYZ/cFcnQjJTwiz3fdbp1tqlOWxTOWi5l98zX7lZaa1dphDcC63pYdLNjd5yevo2NdknHPJ9zn9baxcErfMdZ6TvwGg5503/7WMtL/WL7zj/jrDvX6EWukF2K/dp1xxipuPc7cKdSwsybkHhRIwee8ToOSNGzxkxeu7TJ0YnRidGL/gY3XfuPyGHc//z7na5PSc0VVabbW7KeLh8lZA0L0K+fne3K6/ADsH+DkscXDLnTQqdzX7/V1FWISyqtkdYd8B9LBW0vJb7WElZvwRFUi63mbHWLjHGdJHzS+Np7p+MMRvlXLx+KennCJVCbmXrkRJDvl8DN1lrdxbg68RCuPehlt//uZ3tNdefs5zbnaSs4Dac1XJux6mWw3bRiub1/bcPltP+a+ScfPOS/7x8NyOJ1THHOl+xSP9wHlu0+asi53YzKXL+Vvv9n5fy5r+umrJ61/kr6M8uWB85vawaGWNaWWsnucs7yrkVbYukwdEmaow5Vk5DiX9gu0tZ401KzoTGkhPohOQGgqHk9bOWvP2eJbmPOcUlOeUtLGvtcmPMH3LGbL1TziQ6MsZUUlbPoF4hds3xfbHW7jHGbJLz2cW6zvcpqPc13HFVkXPrs5T9QimccOfSgoxbjlS+C8jNEbfyDjF67BGj54wYPWfE6LlPnxidGJ0YveBjdN95oaz7l5PcnhP834Pcfkf8+fJVQlmfe048jYOLYg/qf/3+b+JZLrzlq8jvsdaaXPx1jiZxa+0gOb/q3C2nYlwl50t9rZzb1f52L4bzoyBvD8hvYH7YRLhNwv/WihNy+Tn3PwxZBlBEWGvTlHWrm/+QEL7eGl+EuZ0xJ/3kBL7L5Qy/UNVaW95aW81aW0POBbOPyUP6RV1Bn4N6uo83uD1tJKe3TXk5DX3fF/DreyUv76v/ufSCXJ5Lu4ZJq8BvazySGGMqyJmgU3ImDSwuiNEjI0YHcMQjRvdMQZ2DfOeFwbk8J6QWUD7C5Wt9LvNlrLXLQyUU4ZwYU0WxgXq0sn79ucLLjHhonfsYze1iUbHWpltre1lrO1prUyQdLWesICtnApiuBfXaLt+vX5F+gQo3BpXv/Un0u3iPJV/eZIwJmb8I42Pl1jq//wvic/b9Ahbp1h7/9bH+xSxWr187zPLg9YWh55vX73lBKszHlq6si+1I+fNfl5fy5r+uMH12vnH3rjXGVHLHFm7pLgvV0zYiY0yysm7Rvt5a+621Nng+iEhj2hU0L8ui7xbQWhG3yrneyslvchqlKsrpaSNlXdD0t85YlMFyfF/c85nvtr7g98X3HcrLOTm/cvO+hntPNyvrnF1gMRPy5HxlXTj95WE+Yo0Y3UGMnndex1TE6OERo8cWMbqDGD1QUYzRC+rc7/8e5LaM+yvoc27MFbkGamvteknfuU9vcG9jyBVjTHH5lcg3/svFh+sFrbVLrLVPKWtyhOAJLXw/GsTqPc5wH5MjbNMyzPIJ7uNRcm6DjkZujiPD7/9w+QuXt1xxf7ny3cZxSX7SCsM3tlN7Y0zIesAYc7yyKruQY4rF4PWbh7tQcCcFyByrKkw6zd1eWKH2P1pZJ7hpobY5zGJ1zIVRoT02a+0+SbPdp2dF2NQ3HughSTPCbBNu8jT/denhxjbzyJ9yJq8pL6mTAideyTYZTS7413nhJtE6O8zyw8HLsugrN3XdSUlCvXYFRTdZVjZuDwbfuIR3umNR+8bWCzdeoe99ifQdaKesod+C35eI52RjTEVJJ0RIOz9872u7CNuEXGedyct84ygWxLkUeeCOO/q0+3SrnJ6/xQUxuoMYPe+I0Q+/QhvHxkChPTZidGL0YEU4Rved+5sZY2rmMY1QZijr3BepjHcIs9yXr7yccz1R5BqoXc9K2iFn5tPv3ckqwjLGVDbGfKeC691zuPkuQBsaY+6JtKExprx7IZArxpgyOWziu9UkeNIO30D3Cbl9rRz84z6e6v4aGMAYc4KkK0PtaK1dLGmM+/S1KG91zM1xLFTW+3BViLyVkPRUFK8Zjm/yhNuNMRGHszHGRDtpw9fuY21JXcJs45the5MiT1yRF9/J6eVSVtITYbZ5Ws7YofuV9aNUsDhJj4ZZ96z7mK6syTW8FKtjLowK+7H5yvvVxpiGwSvdgORx9+lv1tqtYdK5xhhzXPBCY0yipLvcp1GPF1eQ3LFIfb0w7pV0o/t/niZekdOY5HNy8Eq3ofLZ4OWHkZdlcbiyziFPh9nmIUU3Fmk4n8k5zhaS3nOX/W2tXRhme9934DRjzLnBK40xJZU1ccvcEBdGvnNytnOe61FljfUca9+6j2caY9oEr3TjlnDnASmrrF9ojLkw0gvl4VyKKBlj4iT1V9Ywfd2stVs8y1DsEaMTowe/JjF6dsToh09hPzZidAcxuqOoxujfyBnnu5SkdyN1jDXGlHAb4nPkxkfD3aePhrozyBhztrJ6zgfvv0hZd6m9mtMdRIUhDi6SDdTuBdhNkvZJOknSLGPME+6vsZKcmduNMU2MMS9JWqoQgZJxZzM1xvQ/TFmPCWvt33LGF5Kkj4wx7xljfOP4yRhTxhjTyhjzpqQVim7yiR7GmCHGmKuMMZn7GWMqGGPulnSzu+jXoP18F7NnuL/q59dQOT9ClJI0xHfCMcaUMsZcJicYizS5yv/Jmen8GEnjjTHnG2NKuWkcZYw51RjzifuFDnUcVxtjQk6o5PbI8lWYTxtjrvVdYLj5/EFZMxLnxzuS5sipxEcbY+43xmTOqmqMSTDGXGCMGSBpbDQJW2un+B3Dh27a5dx0axhjPpUzbpUkPWet3ZPfgwl6/TWSurtPnzTGvOirqN3jelnSY+76d621a8MktVXSc8aYp9yTrowxicaY7pJucbd5Odb5z4sYHnOhUwSOraecCVFKSRrmfm9KuPlrJOkPSfUk7VXkwG2PpN+NMWf7Ag9jzKly6qNEOZN2vF5gR5F3/eQcW0M5E8VtUd6D9P8krXT/72uMyexpYIw5TU4QlNvJ6GLOy7JonQm/3nCf3mGMedMX6BlnRvUn5Nx6nxEmiWhea62kn9ynZ7qPkW4H/U5ZM9QPMcbc4HdOrOeuP81d/3iI/b9yH89z39NK7r6JxpjX5HxvtuTlWHJhsJz5R4ycTgmXGWOOcl//OEm/KPItq1/I+Y4aST8YY541xmTe4uk2ErY3xnwkJ15EjLkXYw2NMQ/L+Syvd1cNlPRmmH2I0bMjRncQoxOjx1QRiGPzrAgcGzE6MXqRj9HdhuT/uU87SvrVGNPSryyXMMacYIx5RE4cFM1dVs/JGQrneDdd3zm3pDHmWklDFDkGf0DOOftYSZPcODqzodsYU9sYc5MxZqSy3iPvWGuL7J+kNpIWyRlzzfe3V86Ygwf9lh2Sc9tbqaD9fev7xyAvqX7pdc7F9r5t20XY5i93m64h1pWW8+u9/7FvV9ZYTv7La0dxHP1DpJkRtGyspPJB+1WWM0aOb5uNcgbnXy6pld92y6N4j253Pztfmtvcz9dKmijpPvf/5WH2P1fOl9W3/z45PQ32+S27PGifM/1e84CcmV6XB7+GnNvS1gSlvdUvn23DfcZybkW2cn84zeE9qOUeq39ZzvB7Ld/fojyU2Xi/Mmbl/FKZHvSevxVm31wfQ4TXLy3nBOx7rYMhym+2721QOe0v55d33+cVnP/PJZWItH803zu/bbq62/yVh+9tvo85Qr46K8J3IofPI7/HXdDHlpv8LVeY+kVO4LfaLy+7Ffg92iPp6jDp+ra5VdJa9/+dcupH//0vCrFvqt82qdHmPdL+uU3b3Xag37Yf5vV766Z1sZz6wpfeTvfPygmAzvJb1y5o33a+dfkpxx6XxbD5kzNExjchXvuA+3yAnHrJSvokn5+D//u8SVKZHLavLaeBx7fPXgWe3w9KejDMvkdJGuW37SFl1beH5PSS+0vh676Q5SG333E5Qflav3T2KOv8vsctk751rULsX0lOo5r1+9vqHr//OWN/br+b/IX9Tlo5Yx76/jKUPS7dKOmuHNLzbds/BnlL9Usvx88xBuWVGJ0Y3f8zIUYnRpeI0ZcrTP0iYnRi9GISo8uZwHivAste8LnNSuoUzfsqZyJN//pzi5u2lfPDxEM57N9GgXH0ATdfu4Ly9Wm0ZSLWf0WyB7WPtXa8nIuW6yUNkjOGzx45EwelSxon6VU5MyzfYJ1f1YsFa+0+a+0dcrrz95czA/pRkirICUL/knP7V2Pr/FqVWy9LelBOD4P5cgqvL80Rkm6TU5kF9Iyw1mbICRy/lhMUxssZJL6uIk+iEukYP5N0kZyL4m1yKpWFkp6UE1xG6p0ha+1wOb0zXpUzDtNuOWM8rZHza+xdbtr++4xxX/NPOV/86n7H4b/dajlj2PVR1jh0O+RUbE2t04Mm36wzw+/pcsr4z3IqlnJyKvflci64/6esHnTRpL1VzknqdjnlZbucz3qdnJ4b7a21j4VNIJ/cMnydpKslDZPzw1JF93GYpCtz+b29Xs5tUTPllJGdci4YbrbW3mKtDb7V1TMxPOZCp7Afm3WGLDhJTtA0S07dVkZO3fmJpJOstd+G29+1TM5t6R/JucAvLadu/EpSE2ttcK+1wuQbv/+jnnjFn7X2Fzl1zq9y6smScoKcfpKaWWtH5if9/PKyLFprD0i6Vs5t2VPknHdKyhl3r4u19mZl3Z6+JZ8vN0pOrCOFnxzRP29r5Izr97CkSW7eysmZcHGgnM/ugzD7HpRzbnxBTmzgC7SHSzrHWvt2Po8lImvtfDm9Hj+Qc+4zcuK9IZJaKWuMPSnE+2qt3WatvUTShXIujFbK+f6Xk3MOHy7ntv9stwcjT6q7f9XklP91cspcTznfy9rW2nzVQ4UZMToxuojRfYjRC4HCfmzE6MToKiYxurX2Ezmx5NtyhsPa66a5w32dD+XME/FVmCTCpdtbTiPzUDmxfxk5d2F1kzPcX0YO+4+X04P6UTnDbG1x83VQTgP3F3LGQf9fNPkqCMZtGQeAIsO95fcWSZ9bazt7mxsUd8YY34myvbX2Ly/zklfGmA8l3S9n4pWQ45Sh4Lm3na6U08PvZmvtwHyk1UxZE84cZ8OPP13sGWPOkdPIvEdSpaLYgAAAxQExOg4nYnTESixjdORdke5BDQAAInPHC/aNTdrTy7xAN8kJfA8o/xNbPeA+jjrCG6eNsibbGUXjNAAAKAqI0QuVWMboyCMaqAEAKKaMMWXkTEhSSc5QDoVqBvPiyBjzlTHmauPMHO9bVt0Y86SccWklaYDNx+QvxpgLlTXbe4EOr1EYuJMYvm+MaW6MiXOXGbcX+VBljakYcsI9AACAwoQY/fA7HDE68qek1xkAAACxZYz5n5xxxKpJinMXP2yt3edVno4gF8iZwVvGmF1yJquJ91s/Vs5kJlExxtSRM7dGOUlJ7uJfrLXD8pXboiFe0v+5fzLGZMgp177xc62kR2M1tiwAAEBBIEb3VIHE6IgdelADAFD8JMiZOMrImXDmulxMMIPYeFDOZGQL5EyOUk7OhD0j5Ex4dZa1dlse0i0p5zNNlDPb/ftyJqA6EkyS9JycycJWKqtheqmcGddbWGvf9SZrAAAAuZYgYnSvFFSMjhhhkkQAAAAAAAAAgCfoQQ0AAAAAAAAA8AQN1AAAAAAAAAAAT9BADQAAAAAAAADwBA3UAAAAAAAAAABP0EANAAAAAAAAAPAEDdQAAAAAAAAAAE/QQA0UcsaY/sYYa4zpX9hev6Dylt90vX7PwvEyX8aYV9zXfuJwvzYQzBjzu1seO3idFwDAkcHr+JCYOnaIqQEHMTWKExqoAaCYM8bUkfSwpI2SeoTZ5gxjTFdjzO2HNXPwhDGmqjHmVmPMF8aYecaYncaYvcaY1caYH40xV+QynYpuuZljjNlhjNlqjJlqjHnEGFM6wq5d3ce3jTHEIgAAoNArLjG1Maaz26iZ09/ZOaST1zgQsdPVfSSmRpFX0usMAMjRWkkL3MfCpqDyVpiPuSh6VVKcpOettTvDbHOnpBslfeb+oXhbp8AYYI+k/ZJqu3+XGWOGSbraWrsrVALGmLqS/pKU6i7aJamMpObuXydjzFnW2ozgfa21k4wxf0g6T065GxCDYwIAIJLCHF8SUxcNxS2mPiSnsT2cveFW5CcOROwQU6M44RcWoJCz1j5lrT3eWvuU13kJVlB5K8zHXNQYY2pL6iRpn6S+ETZt6T5OLvBMoTAoKWmKpHslNbDWxllrK0iqp6yLqQsk9Qq1szGmpKShci5K1ko6x1pbXlI5SR0lbZfURNIXEfLwifv4eL6OBACAXCjM8SUxdeFXTGPqVdbaGhH+xobaKUZxIGKHmBrFAg3UAFC83SHpKEm/WWvTQ21gjKks6Rj36ZTDlTHkjzGmhDGmvduDJVodrLUtrbU9rbVLfQuttcuttV2U1TB9ozEmOcT+t0hq5P5/lbX2T3f/Q9bawZLuctddaIw5K0wefpOULukkY0ybPBwDAADA4UJMnSUWcWDM5TM2LsqIqVEs0EANFHI5TKjyl7uuq3HcYYyZbIzZZozZboyZaIy5MRev0ckYM97dZ6ubxp3GGBNt3owx1Ywx+93ll+aw/0vudotze8wxyPNyN+3O0RyX37rKxpjbjTFD3PHW0o0xe4wxK4wxXxpjWkV6/Rzydp0xZpgxZr37Hm4xxiwyxvxsjLnPGFM2yvSMJN/4d1+GWH+0McbKCWh8ZgWNPfdsXo8n1vL73rvfkVvd70W2cpPLcpdqjHnfGPOvccba22WMmW+M6W6MSYn5QYfOw4nGmG6SlksaJafXc1SstaNz2MT/ltTmIdbf4j6OttZODLH+a0nL3P9vDpOHfZK+c5/emUN+AADIF2LqmOeZmDprfZGKqWMg33FgLMUiNi7K1xnE1CguGIMaKB6OkvSDpMskHZAzBlhFSa0ktTLGHGOtfSF4JzfY+kzSre4iK2mLnAapFpLaK8LYY6FYazcYZxysiyTdJOnnUNu5r+0L9AfmNv2CyHOU/k+S7708KGmb+3+K+9fRGPM/a+0H0SRqjOmrrGOSpB2SSkk62v27RNKvcgKv3GooqY77f6hb9I6VtF5SBUnl5dyyGDxO3LQoXq+g5fm9N8YcJWmQpOvcRcHlpp2c4w/LGNNJTtkr4y7aK2fsvuPcv1uNMVdba4fn4dgiMsZUk3S9nCC/qd+qDAVeDMXKHr//jwrKSzlJvt4Zw0LtbK21xpjfJd0j6dwIrzNGTo+k8/KeVQAAYoaYmpg6lOIWU+dZjOPA/OQj1rFxUb/OIKZGkUcPaqB4uE/Oia+zpErW2nhJyXLGBpOkZ40xx4TY7wFlBXA9JFWz1laRVEXOjMDXyQnQo+WbnOESY0xCmG3ayPl12yqKYFoFl+fcSpP0opyAo5z72nGS6kvq7m7zrjGmSW4TNMacLueYDkl6QlJVa21Fdyy3RDmBxufKIbAJ4Uz3cZW1dl3wSmvtb9baGpKGuIsGhxh77vcoX7Mg5ee9f0xZQeO7kpLc/StLelrOeHlheycZY86RU66PkvSmnLIbJ+ci5HhJ38i5gP0mUg+HaBhj4owxHY0xv0paI+l9OQH4HknfSrpCUg1r7exYvF6Qdn7/zwlad4Ky4oe5EdLwrathjKkSZhvf+IzVjTHHR5VDAABij5iamDqU4hZT+yQZY6a7vXV3G2OWGmO+MMa0i7BPLOPAqBRwbFzUrzOIqVHk0YMaKB4qyxlTNvO2fWvtamPMNZKWSqol6Vo5M09Lktxb23y/Eg+01j7gt+9WSS+62zyZh/z8LGmrpHj3dXuH2OYm93G8/xi4kRRwnnPFWpvtWKy1Vs5tbP8zzqQh97l/XXKZbGv38U9r7ZtBaW+WNNz9i5ZvkpZ/ctjOF2jNyMNrHDZ5fe+NMeUl+SYH+sxa+4jf/tskdTPGlFFW2QpgjCkh6SM5wfg9IfKxQNK1xpif5ASfD0v6X16O0e3N1FZOb5Cr5QSjknOhNUrOZDPfufkuEO4FsO/9GmutXRC0SS2//9dESMp/XS2F6M1irV1kjNkhp8fRaZLmR51hAABih5iamDqUYhVT+yknp4E3Q05jaD33r5Mxpp+kO621B4L2iVkcmBuHKzYu6tcZxNQoDuhBDRQP40ONKWut3SvpD/dp46DV58rpISFJL4VJ93UF3uqfK9baPXJ+6ZWyguZM7kn6WvdpND09CizPMfSr+3h6FPtscR+T3FvEYsUXQG4Mt4ExprSkk9ynM2P42l4I996fK6mS+/+rCu0dObfxhnKmnAlvNknqE+H1fb2cor61zhhzgjHmNTm3m46W0/unoqRZkh6VlGytPcta26+AG6dLyPlO1pTzPbo/xGYV/f4P954Fr6sYditps/tYK8I2AAAcDsTUWYipsxS3mNrXW/hkSWXd3r6+oTv+dLe5VdJ7IfaNdRwYUmGJjf0UhesMYmoUafSgBoqHyRHWpbmPwbdW+SY+W2WtXawQrLVbjTHTlTXOWDQGyPl1uY0xpp61dpnfuoslJcgJeoeE2Decgs5zrhhj6ku6V87YfA3kBEvBP/jVCd4vgpFy3osmksYaYz6TNCroPcuLJPcxUo+FhnLG5bNyAr5sjDEV5ASCzd2/6pI+t9Z2zmf+opbH9943Lt3KcO+ptXa7W27OCLHaV5biJaWZ8HMGlXYfo5o53BhznZzJZHyWy5mAZ5C1dl40acVAdznfT0m6r4CGDwmWLuc9S8ppQwAAChgxtYuYOkCxiqndcYyHBy07KGmCMeY8Sd/LGd7lXmPMB9baRYczf17FxsXgOoOYGkUaDdRA8bA9wjrfbVmlgpZXcx8j3ZolSavzlCNpnJxbourJmbjlZb91vh4gQ621W6JIs6DznCNjzBWSvlLWBBaSM4nGHjkBaWk5t4eWz22a1tolxpgukj6Rc0vWae5rbZTTY+BLST+7t5lFwzdDeaQJbny3Ii51b+kMJVHObWlr5UzwclGU+YiJfLz3viAtTZGFK1e+Xgil5FxI5CQuF9uE2363nAvMrw9347Qx5m1l9Zh+yFrbN8ym/vVNuQhJ+q+LVEftdh/LRtgGAIDDgZg6EDG1o1jF1JFYaw8ZYx6V00BdQs6kku/6bRLrODCUwx4bF5PrDGJqFGkM8QGgQLiBn+9Ww8xbEo0xVSVd6D6N5lZEz7l57y8ncBklZxKdctbaeGttdXdylGvykra1dpCcX7zvljRY0io5Ac+1kn6U9LcxplLYBELz3eZVOcI2vmA60q2IayXVsdbWkjP222EXo/c+2osRH98topOttSY3f1GmP0zOxcoiOUHn45JmG2PmGGOeNMZE1SM7L4wxb0ryjZn3qLX2/Qib+wfgtSNs578uUtDu64m2OcI2AAAckYipo0NMnX9ur/pN7tP6QatjHQeGclhj42J0nUFMjSKNBmrgyLXBfYwUWORmfSS+YPkYY0wr9//r5PxCvFFO8BGNWOTZ1/sl0i/L8WGWXyhnjLEMSZdYa/+21u4O2qZGDnkLy1qbbq3tZa3taK1NkXS0nPH/rJxbwrpGmaRvnLxIM2f7bksLG0xba/daa3PqYVPQ8vPe+96HnMZjC1dufLO1F0hDsbV2vbX2JWvtsXJ6+nwsJ7BsKKmbpGXGmLHGmLvdADqmjDFvyZl9XJIet9a+k8Mu/8mZmEZuHsPxrVtnrY10S6yvfIYd1xEAgEKMmDo8YurAfBWGmDq/Yh0HZuNBbFxcrjOIqVGk0UANHLmmuY/JxpgGoTZwexc0y+sLuL++T3Sf3hT0+JXNPit0TmKR5wxfGmH2L6GscfmC+fZZYK0NN9HF2RFeOyrW2iXW2qfk3I4oSedEmYTvNrjgng+SMmfF9k30MyvqDB5e+XnvfTOp1zXGpIbawB0TMFy5Ge8+1jDGhCsbMWGtnWStvU9OkHuFnDEA98mZkKWnpLXGmKHGmI7GmEi3VeaKO6zHo+7Tx621b+Uij7uU9Z6cHyZdo6xJXIaH2sbdrqKc210l54IHAICihpg69P7E1EWUWyZ88VnAuMqxjANz4zDFxkX+OoOYGsUBDdTAkWuEsgLL58Js87iiH083mG/G4euMMSdJahW0PBqxyPM/7uMVJvQsFLco/GQsvvHkjjXGZOstYow5RdINEV47JHcG9kh8v+AfirhVdmPcx5PDvIb/OGqFvTdHft774XLGkJOkp8Ns85DCj6M3WpJvAqH33FnawzLGROpdkyvW2n3W2h+ttVdJqinnNtXxcnpKXSxnjLz1xpiBxpg8zdTtNk77D+uRY+O0n8/dx/bGmJYh1l+jrIu4SN/15nJikQPKCtABAChKiKmJqYtMTB3mswpe74sJD0n6JcRmsYoDc62AY+PicJ1BTI0ijwZq4Ajl3rbkm2TlFmPM+75bpIwxlYwxz8k5yW7J50sNlvMrt29sL0maZ62d7lGev3IfT5DUO2j/h+RMqhLuNrThcgK1KpIGGWNqu/uWNsZc666PdhIQSephjBlijLnKGOObtEbGmArGmLsl3ewu+jXKdMfLCVJKSzolxPotygrUr3Z7usSEMSbVGGPdv64xSDLP7721dqekN9yndxhj3vQFd8aYisaYJ+Tc6pkRZv8DcoLgA3J6a4wxxpxljMmcJMkYU9+9zXCqnNm/Y8Zam+Hepnq6nBnFu8oJZCvImSzp2GjTDBpz+uFcDOsR7HNJcyQZSd8ZY85y0y1hjLlG0qfudsOstSMjpOO7qJlhrd0RZR4AAPAcMTUxtYpWTF3XGDPFGHOXG78a93VKGGf4mGFyeipLUi9r7YIQacQqDsyTAoiNi8N1BjE1ijwaqIEjW3dljWn3f5I2GGPS5QSTL8kJhH/KzwtYazOU9cu777al/Ezkkq88u0GSb/8ukjYZYzLkBA3vSuolaWiYfRcpq0fBlZJWG2O2SNrhvu4OSQ/m4ZhKyelp8K2cX/63u3naLufWtdJyZnB/NZpErbXblBWAXxpi/SE5s2JL0rOSdhpj1rl/IW/Z80oM3vs35by/kjPe8ka33GTIGZNwkLI+9z0hXn+knM9ou5wA8E8579cmY8weSUvkfFbNlfdJUnJkrV1qrX3RWnuMpNbua0YVhBpjUpQ15vQhSU/4fe6h/h4NTsMNpi+VtFzOmHp/GmN2Stopp0xVkjMGY6ccsuMrl19G3AoAgMKNmJqYukjE1K5T5fyAsETSbmPMRkm75Awj4xuao5/CfAYxjAPzLRaxcTG5ziCmRpFHAzVwBLPWHrLW3iynN8EkOb/8l5QzltbdysOtdWH439p1SNIXeU0oRnnuLCcQn+XuX0JOz4hrrbURg2Fr7ZPua09x9y0l5xf71+TM3h3tLNWS04PlQUk/SJov5xf0CnImsBkh6TZJ7dxf6KPVy328wddDIsh9kt6TE2CWlFTd/ZsXYtto+E8EMimfaUnK33vvBtLXyrmA8u1fUs4YjF3cMpXgbr4lTBo/yplk50U3jR3uPnvl3ObaR06Pk2iGysgza+1Ea+291tppOW8doETQ/9Vz+KsQ5vWXyxlv8SVJc+UEzPslTZczrnUr92I6JGNMfTkT3+xWjG7/BADAC8TUxNQqOjH1ekkPyGnInCdneIoEOTHcfEl9JZ1urb0t0tjm+Y0DC0I+YuMifZ1BTI3iwlhbYB29AAAec28xXCjn9re21toxOeySU3pl5QQ/n1trO0fY7lk5FwnjrLVn5Oc1Dwf3QmOlnLESb7bW5qdHEnLBGPO8nCC8n7X2Nq/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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 20), constrained_layout = True)\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "LineWidths = 3.5\n",
    "\n",
    "xlabel = 'avg ideological composition of neighborhood \\n (normalized by the null model)'\n",
    "ylabel = 'ideological group'\n",
    "\n",
    "CmapValue = 'Greens'\n",
    "\n",
    "LabelValues = ['SC', 'C', 'M', 'L', 'SL']\n",
    "\n",
    "####################################################################################################\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 1)\n",
    "sub.set_title('A. The structure of opinion homophily \\n ($ t_{1} $)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBefore / NullModelBefore, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 2)\n",
    "sub.set_title('B. The structure of opinion homophily \\n ($ t_{2} $)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsAfter / NullModelAfter, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 3)\n",
    "sub.set_title('C. The structure of opinion homophily among younger \\n individuals ($ t_{1}, $ age < 20)', \n",
    "              fontsize = TitleSize-5)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBeforeYoung / NullModelBeforeYoung, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(2, 2, 4)\n",
    "sub.set_title('D. The structure of opinion homophily among elder \\n individuals ($ t_{1}, $ 50 < age)', \n",
    "              fontsize = TitleSize-5)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBeforeOld / NullModelBeforeOld, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31133424-b3b8-412e-ab8b-3ebd0a7dafe3",
   "metadata": {},
   "source": [
    "# The structure of opinion homophily across gender groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0daf21b8-c4f8-4c92-98f0-680587bd97a4",
   "metadata": {},
   "source": [
    "# Among females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bb822181-5149-43d6-9865-5ceab604b108",
   "metadata": {},
   "outputs": [],
   "source": [
    "MaskGender = ( X['sex'] == 1 ) \n",
    "\n",
    "FemaleUsers = set(np.arange(N)[MaskGender])\n",
    "\n",
    "masks = [MaskStrongConsBefore & MaskGender, \n",
    "         MaskModConsBefore & MaskGender, \n",
    "         MaskModBefore & MaskGender, \n",
    "         MaskModLibBefore & MaskGender, \n",
    "         MaskStrongLibBefore & MaskGender]\n",
    "\n",
    "N = X.shape[0]\n",
    "n = sum(MaskGender)\n",
    "\n",
    "NullModelBeforeFemale = []\n",
    "\n",
    "for i in range(len(masks)):\n",
    "    \n",
    "    mask = masks[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBeforeFemale.append(sum(mask) / n)\n",
    "    \n",
    "NullModelBeforeFemale = np.array(NullModelBeforeFemale)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in masks:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBeforeFemale = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) & FemaleUsers\n",
    "        \n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "        \n",
    "        if FriendsBeforeNumber  > 0:\n",
    "            \n",
    "            Composition_atomic = []\n",
    "\n",
    "            for Group in IdGroups:\n",
    "\n",
    "                Intersection = Group & FriendsBefore\n",
    "\n",
    "                Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            pass\n",
    "                \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBeforeFemale.append(Composition)\n",
    "\n",
    "#CompositionsBeforeFemale = np.array(CompositionsBeforeFemale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a30507ab-52fb-44fd-8af8-9614d15b4f3b",
   "metadata": {},
   "source": [
    "# Among males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f2b8c816-b099-488e-8bae-0a75997412ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "MaskGender = ( X['sex'] == 2 ) \n",
    "\n",
    "MaleUsers = set(np.arange(N)[MaskGender])\n",
    "\n",
    "masks = [MaskStrongConsBefore & MaskGender, \n",
    "         MaskModConsBefore & MaskGender, \n",
    "         MaskModBefore & MaskGender, \n",
    "         MaskModLibBefore & MaskGender, \n",
    "         MaskStrongLibBefore & MaskGender]\n",
    "\n",
    "N = X.shape[0]\n",
    "n = sum(MaskGender)\n",
    "\n",
    "NullModelBeforeMale = []\n",
    "\n",
    "for i in range(len(masks)):\n",
    "    \n",
    "    mask = masks[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBeforeMale.append(sum(mask) / n)\n",
    "    \n",
    "NullModelBeforeMale = np.array(NullModelBeforeMale)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in masks:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBeforeMale = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) & MaleUsers\n",
    "        \n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "        \n",
    "        if FriendsBeforeNumber  > 0:\n",
    "            \n",
    "            Composition_atomic = []\n",
    "\n",
    "            for Group in IdGroups:\n",
    "\n",
    "                Intersection = Group & FriendsBefore\n",
    "\n",
    "                Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            pass\n",
    "                \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBeforeMale.append(Composition)\n",
    "\n",
    "#CompositionsBeforeMale = np.array(CompositionsBeforeMale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c99928-908d-4ef1-b2e7-fdfbfbfb6f8f",
   "metadata": {},
   "source": [
    "# Figure C1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8eef1d56-96ae-4b95-9b5d-d13649abde85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10), constrained_layout = True)\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "LineWidths = 3.5\n",
    "\n",
    "xlabel = 'avg ideological composition of neighborhood \\n (normalized by the null model)'\n",
    "ylabel = 'ideological group'\n",
    "\n",
    "CmapValue = 'Greens'\n",
    "\n",
    "LabelValues = ['SC', 'C', 'M', 'L', 'SL']\n",
    "\n",
    "####################################################################################################\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(1, 2, 1)\n",
    "sub.set_title('A. The structure of opinion homophily among \\n females ($ t_{1} $)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBeforeFemale / NullModelBeforeFemale, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "sub = fig.add_subplot(1, 2, 2)\n",
    "sub.set_title('B. The structure of opinion homophily among \\n males ($ t_{1} $)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBeforeMale / NullModelBeforeMale, \n",
    "            annot=True, cmap=CmapValue, linewidths=LineWidths, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues)\n",
    "sub.yaxis.set_ticks(np.arange(5)+0.5, labels=LabelValues, rotation=0)\n",
    "\n",
    "sub.set_ylabel(ylabel, fontsize=AxisSize)\n",
    "sub.set_xlabel(xlabel, fontsize=AxisSize)\n",
    "\n",
    "####################################################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c4bfc0-e2de-487f-ac89-de9126c60b22",
   "metadata": {},
   "source": [
    "# Analyze cross-ideological (cross-cutting) edges (its (relative) representation decreases, as it follows from the previous figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9e270f-1a12-4237-afe3-c1b1fac33507",
   "metadata": {},
   "source": [
    "# M - the number of edges mupltiplied by two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "9b22deef-0f47-464c-8b5c-cc2d5fec5729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d91695b16f3748b492e669c65c13308d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/458132 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e4c83f379de405ba769b40d0e4fe972",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/489498 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EdgesBefore = A1.nonzero()\n",
    "\n",
    "M = int(len(A1.data) ) \n",
    "\n",
    "CrossCuttingEdges = []\n",
    "\n",
    "for i in tqdm(range(M)):\n",
    "    \n",
    "    (x, y) = (EdgesBefore[0][i], EdgesBefore[1][i])\n",
    "    \n",
    "    OpinionBeforex = X['OpinionBefore'][x]\n",
    "    OpinionBeforey = X['OpinionBefore'][y]\n",
    "    \n",
    "    OpinionAfterx = X['OpinionAfter'][x]\n",
    "    OpinionAftery = X['OpinionAfter'][y]\n",
    "    \n",
    "    TieAfter = A2[x, y]\n",
    "    \n",
    "    # Sequence: Strong Conservative, Strong Liberal\n",
    "    \n",
    "    if ((OpinionBeforex < 0.2) & (OpinionBeforey > 0.8)):\n",
    "        \n",
    "        CrossCuttingEdges.append([x, X['FriendsBefore'][x], X['age'][x], X['sex'][x], X['FolloweesBefore'][x], OpinionBeforex, OpinionAfterx, \n",
    "                                  y, X['FriendsBefore'][y], X['age'][y], X['sex'][y], X['FolloweesBefore'][y], OpinionBeforey, OpinionAftery, \n",
    "                                  TieAfter])\n",
    "        \n",
    "    elif ((OpinionBeforex > 0.8) & (OpinionBeforey < 0.2)):\n",
    "        \n",
    "        CrossCuttingEdges.append([y, X['FriendsBefore'][y], X['age'][y], X['sex'][y], X['FolloweesBefore'][y], OpinionBeforey, OpinionAftery, \n",
    "                                  x, X['FriendsBefore'][x], X['age'][x], X['sex'][x], X['FolloweesBefore'][x], OpinionBeforex, OpinionAfterx, \n",
    "                                  TieAfter])\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        pass\n",
    " \n",
    "\n",
    "ColNames = ['IdSC', 'FriendsBeforeSC', 'ageSC', 'sexSC', 'FolloweesBeforeSC', 'OpinionBeforeSC', 'OpinionAfterSC', \n",
    "            'IdSL', 'FriendsBeforeSL', 'ageSL', 'sexSL', 'FolloweesBeforeSL', 'OpinionBeforeSL', 'OpinionAfterSL', \n",
    "            'TieAfter']\n",
    "\n",
    "CrossCuttingEdgesTable = pd.DataFrame(CrossCuttingEdges, columns = ColNames)\n",
    "CrossCuttingEdgesTable = CrossCuttingEdgesTable.drop_duplicates()\n",
    "\n",
    "#CrossCuttingEdgesTable.head()\n",
    "\n",
    "EdgesAfter = A2.nonzero()\n",
    "\n",
    "M = int(len(A2.data) ) \n",
    "\n",
    "CrossCuttingEdges = []\n",
    "\n",
    "for i in tqdm(range(M)):\n",
    "    \n",
    "    (x, y) = (EdgesAfter[0][i], EdgesAfter[1][i])\n",
    "    \n",
    "    OpinionAfterx = X['OpinionBefore'][x]\n",
    "    OpinionAftery = X['OpinionBefore'][y]\n",
    "    \n",
    "    OpinionBeforex = X['OpinionBefore'][x]\n",
    "    OpinionBeforey = X['OpinionBefore'][y]\n",
    "    \n",
    "    TieBefore = A1[x, y]\n",
    "    \n",
    "    # Sequence: Strong Conservative, Strong Liberal\n",
    "    \n",
    "    if ((OpinionAfterx < 0.2) & (OpinionAftery > 0.8)):\n",
    "        \n",
    "        CrossCuttingEdges.append([x, X['FriendsAfter'][x], X['age'][x], X['sex'][x], X['FolloweesAfter'][x], OpinionBeforex, OpinionAfterx, \n",
    "                                  y, X['FriendsAfter'][y], X['age'][y], X['sex'][y], X['FolloweesAfter'][y], OpinionBeforey, OpinionAftery, \n",
    "                                  TieBefore])\n",
    "        \n",
    "    elif ((OpinionAfterx > 0.8) & (OpinionAftery < 0.2)):\n",
    "        \n",
    "        CrossCuttingEdges.append([y, X['FriendsAfter'][y], X['age'][y], X['sex'][y], X['FolloweesAfter'][y], OpinionBeforey, OpinionAftery, \n",
    "                                  x, X['FriendsAfter'][x], X['age'][x], X['sex'][x], X['FolloweesAfter'][x], OpinionBeforex, OpinionAfterx,\n",
    "                                  TieBefore])\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        pass\n",
    "    \n",
    "ColNames = ['IdSC', 'FriendsBeforeSC', 'ageSC', 'sexSC', 'FolloweesBeforeSC', 'OpinionBeforeSC', 'OpinionAfterSC', \n",
    "            'IdSL', 'FriendsBeforeSL', 'ageSL', 'sexSL', 'FolloweesBeforeSL', 'OpinionBeforeSL', 'OpinionAfterSL', \n",
    "            'TieBefore']\n",
    "\n",
    "CrossCuttingEdgesTableAfter = pd.DataFrame(CrossCuttingEdges, columns = ColNames)\n",
    "\n",
    "CrossCuttingEdgesTableAfter = CrossCuttingEdgesTableAfter.drop_duplicates()\n",
    "\n",
    "CrossCuttingEdgesTable.reset_index(inplace=True)\n",
    "CrossCuttingEdgesTableAfter.reset_index(inplace=True)\n",
    "\n",
    "#CrossCuttingEdgesTableAfter.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "d9885a7e-ed78-4c7b-a027-cc115363ceee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>IdSC</th>\n",
       "      <th>FriendsBeforeSC</th>\n",
       "      <th>ageSC</th>\n",
       "      <th>sexSC</th>\n",
       "      <th>FolloweesBeforeSC</th>\n",
       "      <th>OpinionBeforeSC</th>\n",
       "      <th>OpinionAfterSC</th>\n",
       "      <th>IdSL</th>\n",
       "      <th>FriendsBeforeSL</th>\n",
       "      <th>ageSL</th>\n",
       "      <th>sexSL</th>\n",
       "      <th>FolloweesBeforeSL</th>\n",
       "      <th>OpinionBeforeSL</th>\n",
       "      <th>OpinionAfterSL</th>\n",
       "      <th>TieAfter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5414</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5700</td>\n",
       "      <td>49</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>425</td>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1735</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.35</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2186</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  IdSC  FriendsBeforeSC  ageSC  sexSC  FolloweesBeforeSC  \\\n",
       "0      0  5414               28     32      2                 78   \n",
       "1      1  5700               49     32      2                 80   \n",
       "2      2   425               50     36      2                193   \n",
       "3      3  1735               54     42      2                 21   \n",
       "4      4  2186               35     32      2                 73   \n",
       "\n",
       "   OpinionBeforeSC  OpinionAfterSC  IdSL  FriendsBeforeSL  ageSL  sexSL  \\\n",
       "0             0.17            0.16     2               24     32      2   \n",
       "1             0.00            0.00     2               24     32      2   \n",
       "2             0.17            0.19     9               72     32      1   \n",
       "3             0.17            0.35     9               72     32      1   \n",
       "4             0.04            0.04     9               72     32      1   \n",
       "\n",
       "   FolloweesBeforeSL  OpinionBeforeSL  OpinionAfterSL  TieAfter  \n",
       "0                 85             0.85            0.87       1.0  \n",
       "1                 85             0.85            0.87       1.0  \n",
       "2                 99             0.94            0.94       1.0  \n",
       "3                 99             0.94            0.94       1.0  \n",
       "4                 99             0.94            0.94       1.0  "
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CrossCuttingEdgesTable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "3a9d45b7-f1d2-423e-923c-ab847ac114b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CrossCuttingEdgesTableAfter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaf21b5-80e1-48d1-b4be-b01fc63437aa",
   "metadata": {},
   "source": [
    "# Try to find any differences between the general population of an ideological group and those who has cross-ideological connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "d56528d4-3b6c-43cf-9ee7-52ac348f5261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4850846.0\n",
      "\n",
      "1.7039577552334051e-289\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongCons]['FriendsBefore'], \n",
    "                    CrossCuttingEdgesTable['FriendsBeforeSC'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "09361a2b-b6ea-4e17-aff4-c1051edfc928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "888135.0\n",
      "\n",
      "1.0921448770618012e-99\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongLib]['FriendsBefore'], \n",
    "                    CrossCuttingEdgesTable['FriendsBeforeSL'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "fa01000d-a8cb-4eb9-8348-43d005f3e48a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8567539.0\n",
      "\n",
      "3.5666880235160343e-09\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongCons]['FolloweesBefore'], \n",
    "                    CrossCuttingEdgesTable['FolloweesBeforeSC'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "ee6f9bb7-ef6e-4207-901f-6fd7374a2be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1436364.0\n",
      "\n",
      "6.964407394295606e-05\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongLib]['FolloweesBefore'], \n",
    "                    CrossCuttingEdgesTable['FolloweesBeforeSL'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d6d34d-c597-4491-b00e-41e55e6c54d0",
   "metadata": {},
   "source": [
    "# - Those who have cross-cutting friendship connections tend to have more friends overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e6b77be7-35a1-47bb-b907-776fd13362f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11952202.0\n",
      "\n",
      "4.466690221328983e-93\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongCons]['age'], \n",
    "                    CrossCuttingEdgesTable['ageSC'], \n",
    "                    alternative=\"greater\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "eaa7b271-dd44-4c3b-a2f3-8ff86c908981",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1378858.0\n",
      "\n",
      "9.842298663297476e-26\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongLib]['age'], \n",
    "                    CrossCuttingEdgesTable['ageSL'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a99343-15cc-4690-94b6-d01a733a9f41",
   "metadata": {},
   "source": [
    "# - Those Strong Conservatives who have cross-ideological connections tend to be younger. Those Strong Liberals who have cross-ideological ties  are older (age-based homophily?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "74eb2e64-518b-4e91-9998-beeefa42cb9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8484634.5\n",
      "\n",
      "2.4508739527199563e-19\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongCons]['sex'], \n",
    "                    CrossCuttingEdgesTable['sexSC'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "ec545905-4c70-4aab-8bb1-2a9e7f6055a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1715512.5\n",
      "\n",
      "0.2892556813334779\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskStrongLib]['sex'], \n",
    "                    CrossCuttingEdgesTable['sexSL'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346b6ee4-235e-4a40-af8f-8694522dfe1d",
   "metadata": {},
   "source": [
    "# - Among Strong Conservatives, males are more prone to establish cross-cutting connections."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37200c58-baaa-4581-9d8d-b6815d66a322",
   "metadata": {},
   "source": [
    "# Let us try to identify the patterns of the cross-cutting ties evolution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "fbfbc0ae-bc7e-4faa-b5b9-641b412a355d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cross-ideological edges at time t1: 2691\n",
      "\n",
      "Number of cross-ideological edges at time t2: 2800\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of cross-ideological edges at time t1: {CrossCuttingEdgesTable.shape[0]}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of cross-ideological edges at time t2: {CrossCuttingEdgesTableAfter.shape[0]}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae8b034-6d78-46af-9baa-3b39ea741573",
   "metadata": {},
   "source": [
    "# Let consider removed cross-ideological ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "f6913a64-a5c0-46f3-a0b1-2085e87fc491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of deleted cross-ideological edges: 84\n",
      "\n",
      "Number of deleted cross-ideological ties whereby nodes changed their ideological groups: 19\n"
     ]
    }
   ],
   "source": [
    "NDeletedCrossId = sum(CrossCuttingEdgesTable['TieAfter'] == 0)\n",
    "\n",
    "print(f'Number of deleted cross-ideological edges: {NDeletedCrossId}')\n",
    "\n",
    "print('')\n",
    "\n",
    "OpChange = (CrossCuttingEdgesTable['OpinionAfterSC'] > 0.2) | (CrossCuttingEdgesTable['OpinionAfterSL'] < 0.8)\n",
    "\n",
    "NDeletedCrossIdOpChange = sum((CrossCuttingEdgesTable['TieAfter'] == 0) & OpChange)\n",
    "\n",
    "print(f'Number of deleted cross-ideological ties whereby nodes changed their ideological groups: {NDeletedCrossIdOpChange}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d9484d-13c3-434b-864e-fb31136fd29e",
   "metadata": {},
   "source": [
    "# Estimated probability of cross-ideological edge removing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "c8339843-7e98-491f-8d77-49926a317bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.027\n"
     ]
    }
   ],
   "source": [
    "OpChange = (CrossCuttingEdgesTable['OpinionAfterSC'] > 0.2) | (CrossCuttingEdgesTable['OpinionAfterSL'] < 0.8)\n",
    "\n",
    "nom = (NDeletedCrossId - NDeletedCrossIdOpChange)\n",
    "\n",
    "denom = CrossCuttingEdgesTable[~OpChange].shape[0]\n",
    "\n",
    "print(round( nom / denom, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "09580acb-7e09-4e3c-aef4-9fed9b4dc5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.021261713026691102, 0.034300872186459205)"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_confint(count=nom,    # Number of \"successes\"\n",
    "                   nobs=denom,    # Number of trials\n",
    "                   method='wilson',\n",
    "                   alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31047270-3d83-4e4e-8680-d09c95f1def7",
   "metadata": {},
   "source": [
    "# Probability of edge removing (the system average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "bfabcefd-6d24-49b2-b2e9-b36695dcb0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01991565749609283"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltaA = A2 - A1\n",
    "\n",
    "NDeletedAdges = int(deltaA[deltaA < 0].sum() * (-1) / 2)\n",
    "\n",
    "NEdges = A1.sum() / 2\n",
    "\n",
    "nom = NDeletedAdges\n",
    "\n",
    "denom = NEdges\n",
    "\n",
    "nom / denom\n",
    "\n",
    "#print(f'Probability of edge deleting: {round(NDeletedAdges / NEdges, 2)}')\n",
    "\n",
    "#print(f'Number of new ties: {int(deltaA[deltaA > 0].sum() / 2)}')\n",
    "\n",
    "#print(f'Number of deleted ties: {int(deltaA[deltaA < 0].sum() * (-1) / 2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "85a1cf2f-d5dc-49ac-993f-5cb33d3bec74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.019351523804015933, 0.020495893039207333)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_confint(count=nom,    # Number of \"successes\"\n",
    "                   nobs=denom,    # Number of trials\n",
    "                   method='wilson',\n",
    "                   alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23678a64-d57a-4b5f-9981-bbc300c647d3",
   "metadata": {},
   "source": [
    "# Let us now focus on newly appeared ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "c2611452-0d22-4a05-afe6-da34d79264c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of newly appeared cross-ideological ties: 193\n",
      "\n",
      "Discard those thies wherebe opinions were changed: 193\n"
     ]
    }
   ],
   "source": [
    "NNewCrossCuttingEdges = CrossCuttingEdgesTableAfter[CrossCuttingEdgesTableAfter['TieBefore'] == 0].shape[0]\n",
    "\n",
    "print(f'Number of newly appeared cross-ideological ties: {NNewCrossCuttingEdges}')\n",
    "\n",
    "print('')\n",
    "\n",
    "OpChange = (CrossCuttingEdgesTableAfter['OpinionBeforeSC'] > 0.2) | (CrossCuttingEdgesTableAfter['OpinionBeforeSL'] < 0.8)\n",
    "\n",
    "NNewCrossCuttingEdgesNoOpinionChanges = CrossCuttingEdgesTableAfter[(CrossCuttingEdgesTableAfter['TieBefore'] == 0) & ~OpChange].shape[0]\n",
    "\n",
    "print(f'Discard those thies wherebe opinions were changed: {NNewCrossCuttingEdgesNoOpinionChanges}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647d93f3-d423-42d4-8357-33a9da334c9f",
   "metadata": {},
   "source": [
    "# - The number of cross-ideological edges is going up (+ 193 - 84)!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359f967c-380f-4869-904d-f8f79af24a47",
   "metadata": {},
   "source": [
    "# The number of potential cross-ideological ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "583dc9fd-1525-4dde-9841-4481c7d60376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135167445"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum((X['OpinionBefore'] <= 0.2) & (X['OpinionAfter'] <= 0.2)) * sum((X['OpinionBefore'] >= 0.2) & (X['OpinionAfter'] >= 0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef59e75b-04c1-4604-bae7-c6acc9130068",
   "metadata": {},
   "source": [
    "# Estimated probability of croos-ideological tie appearing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "9eb77aff-5168-476d-9a78-4c8f5d5fb5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4278840149790212e-06"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OpChange = (CrossCuttingEdgesTable['OpinionAfterSC'] > 0.2) | (CrossCuttingEdgesTable['OpinionAfterSL'] < 0.8)\n",
    "\n",
    "\n",
    "nom = NNewCrossCuttingEdgesNoOpinionChanges\n",
    "\n",
    "denom = (sum((X['OpinionBefore'] <= 0.2) & (X['OpinionAfter'] <= 0.2)) * sum((X['OpinionBefore'] >= 0.2) & (X['OpinionAfter'] >= 0.2)) - CrossCuttingEdgesTable[~OpChange].shape[0])\n",
    "\n",
    "nom / denom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "56e01f23-0c5c-4b1c-92fb-420e7052a52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.240146072375681e-06, 1.6440423815411227e-06)"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_confint(count=nom,    # Number of \"successes\"\n",
    "                   nobs=denom,    # Number of trials\n",
    "                   method='wilson',\n",
    "                   alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1145611c-c759-41a8-adf9-d3d2db6a9ab8",
   "metadata": {},
   "source": [
    "# Estimated probability of tie appearing (the system average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "34ed05d4-dd1c-4a8f-a23e-06ae1adf7c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.2194695552478775e-05"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltaA = A2 - A1\n",
    "\n",
    "nom = int(deltaA[deltaA > 0].sum() / 2)\n",
    "\n",
    "denom = (N*(N-1)/2 - A1.sum()/2)\n",
    "\n",
    "nom / denom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "8c07fdf0-f62c-4b51-bf35-45a301060bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.148067073975043e-05, 5.291862319756923e-05)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_confint(count=nom,    # Number of \"successes\"\n",
    "                   nobs=denom,    # Number of trials\n",
    "                   method='wilson',\n",
    "                   alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db426c16-7127-4a51-a114-9aec8d207e0a",
   "metadata": {},
   "source": [
    "# - Cross-ideological ties appear 36 times less often than the system average !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1e00dd-e9a7-492d-9c63-c625f6162845",
   "metadata": {},
   "source": [
    "# Gate keepers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875653d9-7b0f-4da3-ab31-f7230a2840d2",
   "metadata": {},
   "source": [
    "# Those who have at least one Strong Conservative friend and at least one Strong Liberal friends ($ t_{1} $)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "d554d9dd-65ff-48fa-8f92-04468f1d7cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FriendsBefore</th>\n",
       "      <th>OpinionBefore</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>FriendsBeforeStrongCons</th>\n",
       "      <th>FriendsBeforeStrongLib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>0.45</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>0.85</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.50</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>0.75</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0.94</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FriendsBefore  OpinionBefore  age  sex  FriendsBeforeStrongCons  \\\n",
       "0             21           0.45   35    1                        4   \n",
       "1             24           0.85   32    2                        2   \n",
       "2             26           0.50   32    1                        3   \n",
       "3             61           0.75   35    2                        9   \n",
       "4             72           0.94   32    1                        8   \n",
       "\n",
       "   FriendsBeforeStrongLib  \n",
       "0                       2  \n",
       "1                       5  \n",
       "2                       4  \n",
       "3                       5  \n",
       "4                      14  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MaskStrongCons = X['OpinionBefore'] <= OpThr[1]\n",
    "MaskStrongLib = X['OpinionBefore'] >= OpThr[4]\n",
    "\n",
    "N = X.shape[0]\n",
    "\n",
    "StrongCons = set(np.arange(N)[MaskStrongCons])\n",
    "StrongLib = set(np.arange(N)[MaskStrongLib])\n",
    "\n",
    "\n",
    "GatKeepers = []\n",
    "\n",
    "for i in range(X.shape[0]):\n",
    "    \n",
    "    FriendsBefore = set(A1.indices[A1.indptr[i]:A1.indptr[i+1]]) \n",
    "    \n",
    "    FriendsStrongCons = len(FriendsBefore & StrongCons)\n",
    "    \n",
    "    FriendsStrongLib = len(FriendsBefore & StrongLib)\n",
    "    \n",
    "    if (FriendsStrongCons > 1) & (FriendsStrongLib > 1):\n",
    "        \n",
    "        GatKeepers.append([len(FriendsBefore), \n",
    "                           X['OpinionBefore'][i], X['age'][i], X['sex'][i],\n",
    "                           FriendsStrongCons, FriendsStrongLib])\n",
    "    \n",
    "    \n",
    "GatKeepers = pd.DataFrame(GatKeepers, columns = ['FriendsBefore', \n",
    "                                                 'OpinionBefore', 'age', 'sex',\n",
    "                                                 'FriendsBeforeStrongCons', \n",
    "                                                 'FriendsBeforeStrongLib'] )    \n",
    "\n",
    "GatKeepers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "64150bcc-ad04-44dd-9d0c-dd65c70581ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7242550.0\n",
      "\n",
      "1.852775613966565e-256\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskMiddle]['FriendsBefore'], \n",
    "                    GatKeepers['FriendsBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "c4de968a-d546-46d4-a169-9a9e8a4e79cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8687715.5\n",
      "\n",
      "7.854086458062548e-130\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskMiddle]['OpinionBefore'], \n",
    "                    GatKeepers['OpinionBefore'], \n",
    "                    alternative=\"less\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addd9c2c-168e-492c-b2ce-e5be3afbe219",
   "metadata": {},
   "source": [
    "# Those who have at least one Strong Conservative friend and at least one Strong Liberal friends ($ t_{2} $)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "013f164b-c301-4082-96b0-4d6d68479f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FriendsAfter</th>\n",
       "      <th>OpinionAfter</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>FriendsAfterStrongCons</th>\n",
       "      <th>FriendsAfterStrongLib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>0.45</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>25</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>0.75</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.94</td>\n",
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       "      <td>15</td>\n",
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      ],
      "text/plain": [
       "   FriendsAfter  OpinionAfter  age  sex  FriendsAfterStrongCons  \\\n",
       "0            21          0.45   35    1                       4   \n",
       "1            25          0.85   32    2                       2   \n",
       "2            26          0.50   32    1                       3   \n",
       "3            75          0.75   35    2                       9   \n",
       "4            77          0.94   32    1                       7   \n",
       "\n",
       "   FriendsAfterStrongLib  \n",
       "0                      2  \n",
       "1                      3  \n",
       "2                      5  \n",
       "3                      6  \n",
       "4                     15  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MaskStrongCons = X['OpinionAfter'] <= OpThr[1]\n",
    "MaskStrongLib = X['OpinionAfter'] >= OpThr[4]\n",
    "\n",
    "StrongCons = set(np.arange(N)[MaskStrongCons])\n",
    "StrongLib = set(np.arange(N)[MaskStrongLib])\n",
    "\n",
    "\n",
    "GatKeepers = []\n",
    "\n",
    "for i in range(X.shape[0]):\n",
    "    \n",
    "    FriendsAfter = set(A2.indices[A2.indptr[i]:A2.indptr[i+1]]) \n",
    "    \n",
    "    FriendsStrongCons = len(FriendsAfter & StrongCons)\n",
    "    \n",
    "    FriendsStrongLib = len(FriendsAfter & StrongLib)\n",
    "    \n",
    "    if (FriendsStrongCons > 1) & (FriendsStrongLib > 1):\n",
    "        \n",
    "        GatKeepers.append([len(FriendsAfter), \n",
    "                           X['OpinionBefore'][i], X['age'][i], X['sex'][i],\n",
    "                           FriendsStrongCons, FriendsStrongLib])\n",
    "    \n",
    "    \n",
    "GatKeepers = pd.DataFrame(GatKeepers, columns = ['FriendsAfter', \n",
    "                                                 'OpinionAfter', 'age', 'sex',\n",
    "                                                 'FriendsAfterStrongCons', \n",
    "                                                 'FriendsAfterStrongLib'] )    \n",
    "\n",
    "GatKeepers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67637eb6-a424-486a-83fc-9d708a2929be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d5bc6cb4-f1a6-45ec-9206-7a5fe01b8f9f",
   "metadata": {},
   "source": [
    "# Gate keapers tend to have more friends and are rather liberals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e6437d-c0c8-44ef-96ce-0a400f05e7d1",
   "metadata": {},
   "source": [
    "# Age-based homophily (Figure C2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fc4f5366-ff9f-440a-a021-2eb1d05a0646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NullModelBefore = []\n",
    "\n",
    "for i in range(len(MasksAge)):\n",
    "    \n",
    "    mask = MasksAge[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBefore.append(sum(mask) / N)\n",
    "    \n",
    "NullModelBefore = np.array(NullModelBefore)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in MasksAge:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBefore = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) \n",
    "\n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "\n",
    "        Composition_atomic = []\n",
    "\n",
    "        for Group in IdGroups:\n",
    "\n",
    "            Intersection = Group & FriendsBefore\n",
    "\n",
    "            Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "            \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBefore.append(Composition)\n",
    "    \n",
    "    \n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "sub = fig.add_subplot(1, 1, 1)\n",
    "sub.set_title('The structure of age homophily (t1)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBefore / NullModelBefore, \n",
    "            annot=True, cmap='Greens', linewidths=2, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(4)+0.5, labels=['(18-29)', '(29-35)', '(35-44)', '(44+)'])\n",
    "sub.yaxis.set_ticks(np.arange(4)+0.5, labels=['(18-29)', '(29-35)', '(35-44)', '(44+)'], rotation=0)\n",
    "\n",
    "sub.set_ylabel(\"user age\", fontsize=AxisSize)\n",
    "sub.set_xlabel(\"avg composition of neighborhood (normalized by the null model)\", fontsize=AxisSize)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf368e12-03de-495c-bc58-f0846ff37102",
   "metadata": {},
   "source": [
    "# Gender-based gomophily (Figure C3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "01407579-0b1d-4515-a110-acda1f692b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "masks = [MaskFemale, MaskMale]\n",
    "masksNames = ['Female', 'Male']\n",
    "\n",
    "\n",
    "N = X.shape[0]\n",
    "\n",
    "NullModelBefore = []\n",
    "\n",
    "for i in range(len(masks)):\n",
    "    \n",
    "    mask = masks[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBefore.append(sum(mask) / N)\n",
    "    \n",
    "NullModelBefore = np.array(NullModelBefore)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in masks:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBefore = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) \n",
    "\n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "\n",
    "        Composition_atomic = []\n",
    "\n",
    "        for Group in IdGroups:\n",
    "\n",
    "            Intersection = Group & FriendsBefore\n",
    "\n",
    "            Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "            \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBefore.append(Composition)\n",
    "    \n",
    "    \n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "sub = fig.add_subplot(1, 1, 1)\n",
    "sub.set_title('The structure of gender homophily (t1)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBefore / NullModelBefore, \n",
    "            annot=True, cmap='Greens', linewidths=2, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(2)+0.5, labels=masksNames)\n",
    "sub.yaxis.set_ticks(np.arange(2)+0.5, labels=masksNames, rotation=0)\n",
    "\n",
    "sub.set_ylabel(\"user gender\", fontsize=AxisSize)\n",
    "sub.set_xlabel(\"avg composition of neighborhood (normalized by the null model)\", fontsize=AxisSize)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae08089-75e8-427a-949f-fdd2b357fba6",
   "metadata": {},
   "source": [
    "# Where nodal degree assortativity come from (Figure 4)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "15cb52d2-135e-485c-b0b9-aed417f11380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NullModelBefore = []\n",
    "\n",
    "for i in range(len(MasksDegree)):\n",
    "    \n",
    "    mask = MasksDegree[i]\n",
    "    \n",
    "    #print(round(sum(mask) / N, 2))\n",
    "    \n",
    "    #print('')\n",
    "    \n",
    "    #maskName = masksNames[i]\n",
    "    \n",
    "    NullModelBefore.append(sum(mask) / N)\n",
    "    \n",
    "NullModelBefore = np.array(NullModelBefore)\n",
    "\n",
    "#NullModelBefore = np.round(NullModelBefore, 2)\n",
    "\n",
    "IdGroups = []\n",
    "\n",
    "for mask in MasksDegree:\n",
    "    \n",
    "    IdGroups.append(set(np.arange(N)[mask]))\n",
    "\n",
    "\n",
    "CompositionsBefore = []\n",
    "\n",
    "for i in IdGroups:\n",
    "\n",
    "    Composition = []\n",
    "\n",
    "    for member in i:\n",
    "\n",
    "        FriendsBefore = set(A1.indices[A1.indptr[member]:A1.indptr[member+1]]) \n",
    "\n",
    "        #FriendsBeforeOpinions = X['OpinionsBefore'][FriendsBefore]\n",
    "\n",
    "        FriendsBeforeNumber = len(FriendsBefore)\n",
    "\n",
    "        Composition_atomic = []\n",
    "\n",
    "        for Group in IdGroups:\n",
    "\n",
    "            Intersection = Group & FriendsBefore\n",
    "\n",
    "            Composition_atomic.append(len(Intersection) /  FriendsBeforeNumber)\n",
    "            \n",
    "        Composition.append(Composition_atomic)\n",
    "        \n",
    "    Composition = np.array(Composition)\n",
    "    \n",
    "    Composition = Composition.mean(axis=0)\n",
    "    \n",
    "    #Composition = np.round(Composition.mean(axis=0), 2) \n",
    "    \n",
    "    CompositionsBefore.append(Composition)\n",
    "    \n",
    "    \n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "\n",
    "TitleSize = 30\n",
    "AxisSize = 20\n",
    "TickSize = 15\n",
    "\n",
    "sub = fig.add_subplot(1, 1, 1)\n",
    "sub.set_title('The structure of degree homophily ($t_{1}$)', fontsize = TitleSize)\n",
    "\n",
    "\n",
    "sns.heatmap(CompositionsBefore / NullModelBefore, \n",
    "            annot=True, cmap='Greens', linewidths=2, linecolor='k', \n",
    "            annot_kws={\"fontsize\":20}, ax=sub, cbar=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.xaxis.set_ticks(np.arange(4)+0.5, labels=['(1-6)', '(6-12)', '(12-22)', '(22+)'])\n",
    "sub.yaxis.set_ticks(np.arange(4)+0.5, labels=['(1-6)', '(6-12)', '(12-22)', '(22+)'], rotation=0)\n",
    "\n",
    "#[0, 6, 12, 22]\n",
    "\n",
    "sub.set_ylabel(\"nodal degree\", fontsize=AxisSize)\n",
    "sub.set_xlabel(\"avg composition of neighborhood (normalized by the null model)\", fontsize=AxisSize)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b84f133-1afc-42a7-b1ed-5fbe16946645",
   "metadata": {},
   "source": [
    "#  How users' activity varies with their friends-based degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "88432a8b-73b3-48fe-ac32-8735a0806d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################################\n",
      "\n",
      "How users' activity (the number of new friends) varies with the number of friends\n",
      "\n",
      "Group: Few, median value: 0, mean value: 0.65\n",
      "\n",
      "Group: Avg, median value: 1, mean value: 1.02\n",
      "\n",
      "Group: Many, median value: 1, mean value: 1.37\n",
      "\n",
      "Group: Hub, median value: 2, mean value: 2.96\n",
      "\n",
      "########################################\n",
      "\n",
      "How users' activity (the number of deleted friends) varies with the number of friends\n",
      "\n",
      "Group: Few, median value: 0, mean value: 0.12\n",
      "\n",
      "Group: Avg, median value: 0, mean value: 0.27\n",
      "\n",
      "Group: Many, median value: 0, mean value: 0.34\n",
      "\n",
      "Group: Hub, median value: 0, mean value: 0.63\n",
      "\n",
      "########################################\n"
     ]
    }
   ],
   "source": [
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(\"How users' activity (the number of new friends) varies with the number of friends\")\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(MasksDegree)):\n",
    "    \n",
    "    mask = MasksDegree[i]\n",
    "    maskName = MasksDegreeNames[i]\n",
    "    \n",
    "    NewFriendsMedian = int(X[mask]['NewFriends'].median())\n",
    "    NewFriendsMean = round(X[mask]['NewFriends'].mean(), 2)\n",
    "    \n",
    "    print(f'Group: {maskName}, median value: {NewFriendsMedian}, mean value: {NewFriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(\"How users' activity (the number of deleted friends) varies with the number of friends\")\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(MasksDegree)):\n",
    "    \n",
    "    mask = MasksDegree[i]\n",
    "    maskName = MasksDegreeNames[i]\n",
    "    \n",
    "    NewFriendsMedian = int(X[mask]['DeletedFriends'].median())\n",
    "    NewFriendsMean = round(X[mask]['DeletedFriends'].mean(), 2)\n",
    "    \n",
    "    print(f'Group: {maskName}, median value: {NewFriendsMedian}, mean value: {NewFriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "7ddbf936-7ae8-4f27-bb9d-b03807818db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################################\n",
      "\n",
      "How users' activity (the number of new followees) varies with the number of friends\n",
      "\n",
      "Group: Few, median value: 5, mean value: 8.23\n",
      "\n",
      "Group: Avg, median value: 5, mean value: 7.61\n",
      "\n",
      "Group: Many, median value: 4, mean value: 6.83\n",
      "\n",
      "Group: Hub, median value: 5, mean value: 6.88\n",
      "\n",
      "########################################\n",
      "\n",
      "How users' activity (the number of deleted followees) varies with the number of friends\n",
      "\n",
      "Group: Few, median value: 2, mean value: 5.07\n",
      "\n",
      "Group: Avg, median value: 3, mean value: 4.92\n",
      "\n",
      "Group: Many, median value: 3, mean value: 4.91\n",
      "\n",
      "Group: Hub, median value: 3, mean value: 5.26\n",
      "\n",
      "########################################\n"
     ]
    }
   ],
   "source": [
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(\"How users' activity (the number of new followees) varies with the number of friends\")\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(MasksDegree)):\n",
    "    \n",
    "    mask = MasksDegree[i]\n",
    "    maskName = MasksDegreeNames[i]\n",
    "    \n",
    "    NewFriendsMedian = int(X[mask]['NewFollowees'].median())\n",
    "    NewFriendsMean = round(X[mask]['NewFollowees'].mean(), 2)\n",
    "    \n",
    "    print(f'Group: {maskName}, median value: {NewFriendsMedian}, mean value: {NewFriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(\"How users' activity (the number of deleted followees) varies with the number of friends\")\n",
    "\n",
    "print('')\n",
    "\n",
    "for i in range(len(MasksDegree)):\n",
    "    \n",
    "    mask = MasksDegree[i]\n",
    "    maskName = MasksDegreeNames[i]\n",
    "    \n",
    "    NewFriendsMedian = int(X[mask]['DeletedFollowees'].median())\n",
    "    NewFriendsMean = round(X[mask]['DeletedFollowees'].mean(), 2)\n",
    "    \n",
    "    print(f'Group: {maskName}, median value: {NewFriendsMedian}, mean value: {NewFriendsMean}')\n",
    "    \n",
    "    print('')\n",
    "\n",
    "print('########################################')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "a0610cce-5b9d-4b83-a957-9de4faf6cf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25843881.5\n",
      "\n",
      "0.5824651471366951\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskFew]['NewFollowees'], \n",
    "                    X[MaskAvg]['NewFollowees'], \n",
    "                    alternative=\"greater\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "8bab0233-2417-40e0-837b-077bfce3bea2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28069273.5\n",
      "\n",
      "0.0001443965893782859\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskFew]['NewFollowees'], \n",
    "                    X[MaskMany]['NewFollowees'], \n",
    "                    alternative=\"greater\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "e63e7bb1-5d83-4eac-8f91-3025393d4a9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25738797.5\n",
      "\n",
      "0.15384713301661895\n"
     ]
    }
   ],
   "source": [
    "U, p = mannwhitneyu(X[MaskFew]['NewFollowees'], \n",
    "                    X[MaskHub]['NewFollowees'], \n",
    "                    alternative=\"greater\")\n",
    "\n",
    "print(U)\n",
    "\n",
    "print('')\n",
    "\n",
    "print(p)"
   ]
  }
 ],
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